Socio-economic advancement and grid-frequency control of a wind farm-PHS-solar connected EV hybrid electrical system considering imbalance prices

Abstract

The Independent System Operator (ISO) plays a key role in a competitive electrical system by controlling the market with the received bidding from the different market participants. The ISO settled the energy at the location-based marginal pricing (LBMP) where all the participants get economic benefits. In the renewable combined system, the future renewable power production data need to be submitted to the ISO at least one day before the operation. There is always a chance for not fulfilling the contracted power due to the volatile nature of the renewable which creates an imbalance price in the system. In this situation, energy storage systems can alleviate the imbalance issues by supplying additional power to the electrical grid. This study proposes a two-phase scheduling technique for the optimum operation of a wind farm (WF)–pumped hydro storage (PHS)–solar-connected electric vehicle (EV) hybrid system to obtain more economic profit and revenue with a stable grid frequency. In the first phase, the energy level of PHS upper reservoirs has been scheduled to maintain the grid frequency with the presence of a wind farm. Along with the first phase, additionally solar-connected EVs are incorporated in the second phase to maximize the system profit further to get more economic benefit. A modified IEEE 30 bus system is used to effectively assess the aforesaid approach. MiPower software has been used to conduct this work. This proposed method has been compared with the existing method and got better results in all aspects.

Keywords

Frequency control PHS wind farm EVs energy level control location-based marginal pricing

Introduction

In the recent era, the energy demand is increasing sharply due to the massive growth in the industrial sector as well as the developments in the latest technologies. The uses of conventional energy sources are decreasing day by day due to the scarcity of fossil fuels and their negative effect on the environment.¹ Environmental pollution, generation of CO₂, and shortage of energy resources have diverted the power industry to move toward green energy. Globally wind and solar energy are extremely suitable energy sources based on their zero carbon footprints, zero fuel cost, easy availability, and environment-safe nature. Wind energy plays a key role in fulfilling the power demand and has achieved magnificent growth in the manufacturing, commercial, utility, and non-utility sector in India as well as in the world.² Central Electricity Authority (CEA) of India has anticipated growth in wind energy capacity of 40.5 GW and 53.1 GW from 2022 to 2027 and 2027 to 2032, respectively.³ The state and central government are also working toward achieving the massive potential of 195 GW of offshore energy in India. The wind generation capacity of India is rising every year. India has ranked fourth in the world for installing wind-based energy generation capacity. The government of India is encouraging the public as well as private enterprises to incorporate wind energy installation in the country by promoting different financial and fiscal incentives schemes. But there is a major drawback in wind energy, i.e., they are unpredictable which is caused to the mismatch between the actual and forecasted wind speed. This issue can harm the system's profit and can create system risk.

Pump hydro storage is an efficient storage method that pumps the water uphill when the power demand is low by using the excess wind energy and releasing the water from the top reservoir to the downhill through a turbine when there is a requirement for power in the electrical grid. PHS can act immediately according to the fluctuating power demand in the grid. On the other end, electric vehicles can also be used as give and take policy of power from the grid as per the requirement. The power generated from renewable energy can also be converted/ stored into EVs. The spine of e-mobility is the charging infrastructure. Developed and developing countries are shifting toward electrifying mobility. Most countries have imposed policies for a huge adoption of EVs. By 2030, the Govt. of India (GoI) has set a target to electrify 80% of two & three wheelers vehicles, 70% of all its commercial vehicles, 40% of buses, and 30% of private car sales. This goal shows the increasing use of EVs in India.³ This situation forces electrical power producers to think about the proper utilization of EVs in the operation of renewable systems. The swiftness of the electricity grid to renewable generation connection is also increasing day by day. Still, due to its unpredictable nature, poor regulation, and high-level grid network creates huge challenges for the safe and economic operation of grids.⁴ Papers^5,6 have discussed the multisource integrated power generation system which effectively combines the benefits of wind, solar, hydro energy, and battery energy storage system to provide synergistic energy management. The uncertain and irregular nature of renewable sources in microgrids can be mitigated by storing, collecting, and using renewable sources locally. This has been the utmost significant means to ease the issue with renewable energy utilization and depend on grid friendly.^7,8 In the present period, researchers are focusing on multisource power generation with complementary source management. A tool was designed in⁹ to analyze the interactive networks and simulate interconnected power generation scenarios of various renewable sources. WF and PHS technology can be incorporated into the power grid to make the electrical system more secure and stable. Power industries are encouraging this type of technology for the last few decades. However, unpredictable nature and market pricing are two main issues in wind power integration whereas water reserves are the major concerns in PHS generation. The extreme increase in the electric vehicle industry harms the operation of the grid. Almost 350 million electric vehicle expansions are been expected by 2030 around the globe.¹⁰ It has been noted that operations of EVs without regulation rises the electricity pricing and power demands. Moreover, it has been observed that civilian vehicles are parked around 90% of the whole daytime.¹¹ Hence EVs have the potential to contribute toward the microgrid as power regulators in the form of distributed mobile energy storage. The main aim of vehicle-to-grid (V2G) technology is to involve battery vehicles in the grids to serve as energy supply, storage facilities, or controllable loads.^12,13 Ref.¹⁴ focuses on the impact evaluation of the charging and discharging approach of EVs based on operation management of grid accessibility and isolated micro-grid. Paper^15,16 has deliberated a plan for the frequency modulation of EVs. The congestion managing technique is been proposed in^17,18 using EVs for an integrated system. Two decentralized EV charging structures are framed for the load curve scheduling of residential communities, which synchronizes peak shaving and valley filling through EV's charging and discharging.¹⁹ Electricity pricing, availability of charging stations, and battery charging are the main concern of EVs for integrated management scheduling. A real-time charging management system has been designed for the commercialization of PV generation and EV charging service which can ideally control the unpredictable nature of PV generation and EV parking.²⁰ An energy system framework is presented in,²¹ which contains fuel cells, renewable energy sources, and storage devices. The model determines the optimal expanse for energy consumption, wind, and sunlight. Kumar et al.²² have dealt with the integration of fuel cells, photovoltaics, and ultra-capacitor in the grid with the Jaya-based maximum power point tracking (MPPT) method. The author²³ has presented a technologically advanced genetic algorithm to enhance the structure of the solar-wind-fuel cell-battery integrated power system. Paper²⁴ has presented that the integration of hydro and wind power resources creates an imbalance cost for the power-producing companies which can be minimized by inner balancing of the sales and purchases in a real-time electricity market. In the forecasted market, wind generation companies can deliberately commerce to utilize the surrounding possibilities and diminish loss in the worst scenario although with the risk predilections replicated by their utility principles.²⁵ Timothy et al.²⁶ review the effect of imbalance costs in peer-to-peer power markets. Paper²⁷ formulates the problem which occurs due to wind-hydro power generation optimization in the operating background of the electricity market. Ref.²⁸ presents an economic feasibility test of a WF-PHS hybrid system.

From the comprehensive literature study, it is observed that limited research work is available on the scheduling strategies of renewable integrated deregulated power systems. The following points still need to be answered which are solved in this present work: (i) the impact of imbalance price on revenue and profit of a wind-associated power system. (ii) how PHS can reduce the effect of imbalance price in the system. (iii) how the hybrid operation of PHS and EVs maximizes the system profit by maintaining the grid frequency in a safer zone. (iv) how energy levels of PHS play a prime role to boost the system's economic sustainability.

In this work, an efficient operating strategy of the thermal-WF-PHS-solar connected EV hybrid system has been projected, which is different from the functional approach suggested in Ref.²⁹ The proposed operating strategy has been implemented and a comparison has been performed between the existing logic²⁹ and the proposed logic. The evaluation is completed concerning the operating strategy of PHS system energy levels, the effective procedure, and some limitations that are completely dissimilar from the existing logic. The main aim of this work is to exploit the system profit and maintain the grid frequency with proper operating of the energy level of the PSH upper reservoir and batteries of the EV system. The concept of the imbalance price has also been deliberated for measuring the system profit and revenue. The entire work has been divided into two parts, i.e., WF-thermal-PSH operation in Phase-1 and WF-thermal-PSH-solar connected EV operation in Phase-2. A deregulated environment has been created to perform this work. The schematic illustration of the projected hybrid system has been displayed in Figure 1. The main role of this hybrid system has been played by the control station. The control station receives the power from the wind farm, thermal power station, PHS system & EVs and transfers the power to the energy consumers with maintaining the grid frequency. The key highlights of this work are as follows:

The work is conducted in a renewable integrated deregulated system where different market players have participated in the bidding and give the best quality of power at a nominal price.

The imbalance price has been produced in a wind-incorporated system due to the disparity between the real and predicted wind speed.

The PHS system has been operated with WF and thermal power system to lessen the risky effect of imbalance price and to maintain the power demand and availability ratio with uphold the grid frequency.

This work has been divided into two operating phases. In the 1^st phase, the system economy has been enhanced by the operation of the thermal-WF-PHS hybrid system whereas, in the 2^nd phase, solar-connected EVs have been introduced along with the operation of the 1^st phase system to maximize the economic sustainability again.

The novelty of this work is the distribution of the energy level of the PSH upper reservoir (i.e., EL_PHS,max, EL_PHS,opt, EL_PHS,low, EL_PHS,min) & batteries of EVs (i.e., EL_EV,max, EL_EV,opt, EL_EV,low, EL_EV,min) and their operation by looking after the conditions of real and predicted wind power along with the grid frequency.

This work can be performed in any small, large as well as hybrid systems. The work has been conducted using MiPower software and three other optimization techniques i.e., SMA, AGTO, and MFO have been used to compare the results.

Figure 1.

Illustrative diagram of the proposed hybrid power plant.

This work is organized as follows: segment 1 provides the background study along with the literature and main highlights of the work. Segment 2 discusses different mathematical modeling associated with the proposed method. Segment 3 illustrates the problem formulation and objective functions. Segment 4 describes the proposed method for assessing the system economy and societal benefit of deregulated systems with maintaining the grid frequency. Segment 5 combines the results obtained for the different scenarios with the integration of the WF-PHS-solar connected EV system with the existing thermal system. The comparative studies on system profit, revenue & PHS upper reservoir energy level using MiPower software and other optimization algorithms were performed and presented in the conclusions of the work.

System modeling

System modeling is considered to be a vital stage before executing the process, objective, simulation, and output validation.

Wind farm-based power generation model

The irregular and unpredictable behavior of wind creates a hurdle for the incorporation of a wind farm in the existing power plant. This work focuses on the wind speed of a competitive power market and with the wind characteristics graph (shown in Figure 2), power generation corresponding to the wind speeds is measured. The generation of electricity from a wind turbine (WP(h)) is as follows³⁰:

WP (h) = \frac{1}{2} . D_{Air} . R_{SA, WT} . η_{WPP} . V_{W, h}^{3}

(1)

Figure 2.

Wind power generation characteristics.

Here, $D_{Air}$ is the density of air, $R_{SA, WT}$ is the rotor sweep areaof a wind turbine, $η_{WPP}$ is the overall wind plant efficiency, $V_{W, h}$ is the wind velocity at h meter height from ground level.The assumptions taken for this work are as follows: $D_{Air}$ = 1.225 kg/m³, $η_{WPP}$ = 0.49 and turbine rotor radius = 40 m. Figure 2 displays the wind power generation characteristics where cut-in speed, rated speed, and cut-out speed play a vital role. Wind power can generate when the wind speed lies between the region of cut-in speed and cut-out speed. From cut-in speed to rated speed region, the wind power is generated based on the characteristics shown in equation (1). After cut-out speed there will be no production of wind power. In this work, the considered assumptions of the wind farm are as follows: cut-in speed, rated speed, and cut-out speed as 3.5 m/s, 16 m/s, and 25 m/s.

Pump hydro storage power model

The operation of pumped hydro storage models is mainly dependent on the height between the lower and upper reservoirs and the volume of the upper reservoir.

The pumped hydro storage system can operate in three operating modes i.e., pumping, generating as well as idle modes. The pumping mode is operating in the off-peak periods whereas the generating mode is operating in the peak period. The pumping mode is also known as the charging mode and generating mode is called discharging mode. Figure 3 depicts the basic structure of the PHS system. In the generating mode, the generated power from the PHS system ( $E_{G, PHS}$ ) is as follows:

E_{G, PHS} = ρ_{w} . g_{acc} . H_{head} . V_{WFR, G} . η_{Gen}

(2)

Figure 3.

Structure of pumped hydro storage system.

Where ρ_w is the water density, g_acc is the acceleration, H_head is the head height, $V_{WFR, G}$ is the water flow rate at generation mode operation, $η_{Gen}$ is the efficiency of the PHS system in generating mode. The consumed energy at pumping mode operation of the PHS system ( $E_{P, PHS}$ ) is as:

E_{P, PHS} = \frac{ρ_{w} . g_{acc} . H_{head} . V_{WFR, p}}{η_{Pump}}

(3)

Here

V_{WFR, p}

is the water flow rate at pumping mode operation,

η_{Pump}

is the efficiency of the PHS system in pumping mode.

Solar-connected electric vehicle modeling

In a solar-incorporated electric vehicle, the solar photovoltaic cell captivate the energy from the sunlight and transforms the energy into electricity to operate the electric vehicle. The generated energy from the solar PV system depends on solar irradiance and PV panel rating. The battery bank is utilized to store the energy in EVs. The state of charge of the EVs (SOC) directs the state of stored energy in the battery bank.

\frac{{EL}_{SOC}^{h}}{{EL}_{SOC}^{h - 1}} = \int_{h - 1}^{h} \frac{P_{pv}^{h} e_{bb}}{Vo l_{bus}} dt

(4)

Here,

{EL}_{SOC}^{h}

is the battery bank state of charge at a time ‘h’.

P_{pv}^{h}

is the power output from the solar panel.

e_{bb}

is the battery bank efficiency.

Vo l_{bus}

is the bus voltage.

e_{bb} = \sqrt (e_{b b}^{c} e_{b b}^{d})

(5)

Here,

e_{b b}^{c}

and

e_{b b}^{d}

are the battery efficiency during charging and discharging modes.

Objective function

The conversion of regulated to the deregulated environment in the electricity network is performed mainly focusing on customer comfort to providing quality power at a lesser price. More competitions among market participants in the electricity market provide more benefit to the customer. In the renewable-associated power system, there is always a chance for creating an imbalance price situation which puts an economic burden on the generation companies (GENCOs) as well as indirectly creates a price liability on the customer. This imbalance in price conditions occurred in a wind-combined system due to the violation of the power contract between the GENCOs and Distribution Companies (DISCOs) as there is a variance arose between the real and predicted wind speeds (WS_R and WS_P). An electricity grid can uphold constancy and safety if there is a proper matching of produced power and customer requirement with preserving the grid frequency at the secured level. In this work, an electrical system has been chosen with N_B overall buses, N_D load buses, and N_G generators. This study proposes a two-phase suitable technique for the optimum operation of a WF–PHS–solar connected EV hybrid powerplant to maximize the profit and revenue of the system with keeping the grid frequency at a constant value by optimal scheduling of energy level of PHS upper reservoir and EV battery bank. The objective of the proposed scheme has been gained by considering the real and predicted wind speed data, grid frequency, and the upper reservoir energy level of the PHS system. In a renewable combined system, if the real and predicted wind speeds differ after completing a power subsidizing contract between GENCOs & DISCOs based on a wind speed approximation, the ISO may penalize or provide a reward to the GENCOs for their deficiency or additional power delivery. To solve this negative issue, the GENCOs always trying to mitigate the gap between the contracted and real power by taking the additional power from the energy storage devices. The following phases have been proposed in this work to obtain better objective function values.

Phase-1: Maximization of system revenue and profit with maintaining the grid frequency (f_g) by proper scheduling operation of a WF and PHS system.

Phase-2: More improvements in system profit with maintaining the grid frequency by hybrid operation of EVs along with the WF and PHS system.

The mathematical expression of the system profit (SP(h)) with consideration of imbalance price (Price_IM(h)) in Phase-1 is as follows:

Maximize, SP (h) = TRP (h) + Pric e_{IM} (h) - G C_{T} (h)

(6)

Here, TRP(h) and GC_T(h) are the total revenue earned by the generation companies and the total generation cost of the wind-PHS hybrid system at a time ‘h’.The total system revenue has encompassed two parts i.e., thermal power plant revenue (

TR P_{Ther} (h)

) and revenue from the hybrid wind-PHS system (

TR P_{WF - PHS} (h)

). Now, the expression of system revenue is as follows:

Maximize, TRP (h) = TR P_{Ther} (h) + TR P_{WF - PHS} (h)

(7)

TR P_{Ther} (h) = \sum_{i = 1}^{N_{G}} G P_{Ther} (i, h) . LBMP (i, h)

(8)

TR P_{WF - PHS} (h) = (TR P_{PHS} (h) + TR P_{WF} (h) - TR P_{SL} (h))

(9)

Here, GP_Ther(i,h) is the generated power from the i^th thermal generator at a time ‘h’. LBMP(i,h) is the location-based marginal price of. TRP_PHS(h), TRP_WF(h), and TRP_SL(h) are the revenue price of PHS, wind farm, and system loss at a time ‘h’.

TR P_{PHS} (h) = TR P_{gen} (h) - TR P_{pump} (h)

(10)

TR P_{gen} (h) = E_{G, PHS} (h) . LBMP (h)

(11)

TR P_{pump} (h) = P w_{pump, w} (h) . Cos t_{WF} + P w_{pump, g} (h) . LBMP (h)

(12)

TR P_{WF} (h) = P_{WF - g} (h) . LBMP (h)

(13)

TR P_{SL} (h) = ω . LBMP (h) . [P_{grid} (h) - P_{WF - g} (h) - E_{G, PHS} (h)]

(14)

Here,

TR P_{gen} (h)

and

TR P_{pump} (h)

are the revenue of the PHS system in generating and pumping modes at the time ‘h’.

P w_{pump, w} (h)

is the power for the pumping mode from a wind farm whereas

P w_{pump, g} (h)

is power for the pumping mode operation of PHS purchased from the electrical grid.

Cos t_{WF}

is the production cost of wind power.

P_{WF - g} (h)

is the wind power delivered to the electrical grid. ω is the system penalty factor.

P_{grid} (h)

is the scheduled power to be delivered to the grid from the WF-PHS hybrid plant. Equations (6) and (7) are considered the objective functions of this work and these are the maximization problems.

In addition to the 1^st phase scheduling mechanism, the operation of solar-connected EVs is introduced in the 2^nd phase of the proposed method. In the 2^nd phase operation, the mathematical expressions are almost similar to 1^st phase operation except for the following equation:

Maximize TRP (h) = TR P_{Ther} (h) + TR P_{WF - PHS - EV} (h)

(15)

TR P_{WF - PHS - EV} (h) = (TR P_{PHS} (h) + TR P_{WF} (h) + TR P_{EV} (h) - TR P_{SL} (h))

(16)

TR P_{EV} (h) = P_{gen, EV} (h) . LBMP (h)

(17)

Here,

TR P_{WF - PHS - EV} (h)

is the revenue from the hybrid wind-PHS-solar connected EV system at a time ‘h’.

TR P_{EV} (h)

is the revenue earned from the solar-connected EV system.

P_{gen, EV} (h)

is the power generating quantity from the solar-connected EV system. In both phases, the imbalance prices have been considered for measuring the system profit. The system imbalance price is measured based on the predicted and real wind speed data. The deficiency charge rate (CR_D) and excess charge rate (CR_E) have produced the system imbalance price. The imbalance price is ‘–ve’ (negative) when the fine is imposed on GENCOs whereas it is ‘ + ve’ (positive) when the award is given to GENCOs by the ISO. The imbalance price is zero when there is no disparity between WS_R and WS_P. The ‘ + ve’ imbalance price boosts the system profit whereas the ‘−ve’ imbalance price reduces the system economic profit. So, it is desirable to maintain the contact power in the grid by providing the additional power supply from the PHS (if needed). The system imbalance price of a wind-associated electrical network is as follows:

Pric e_{IM} (h) = \sum_{i = 1}^{N_{G}} (C R_{E} (h) + C R_{D} (h) \cdot {(\frac{W P_{P} (i, h)}{W P_{R} (i, h)})}^{2}) \cdot (W P_{R} (i, h) - W P_{P} (i, h))

(18)

C R_{D} (h) = (1 + β) \cdot LBMP (i, h), C R_{E} (h) = 0 if W P_{P} (i, h) > W P_{R} (i, h)

(19)

C R_{E} (h) = (1 - β) \cdot LBMP (i, h), C R_{D} (h) = 0 if W P_{P} (i, h) < W P_{R} (i, h)

(20)

C R_{E} (h) = C R_{D} (h) = 0 if W P_{P} (i, h) = W P_{R} (i, h)

(21)

Here,

W P_{P} (i, h)

and

W P_{R} (i, h)

are the generated wind power for the i^th unit at time ‘h’ based on predicted and real wind speed.

β

is the imbalance price coefficient. The system generation cost is represented as:

G C_{T} (h) = G C_{Ther} (h) + Cos t_{WF} + Cos t_{EV}

(22)

Here,

Cos t_{EV}

is the generation cost of the solar-connected EV system. The system generation cost has three parts i.e., cost of the thermal, wind farm, and solar-connected EV systems. The first two-part are considered for Phase-1 whereas all three parts have been considered for Phase-2.

G C_{Ther} (h) = \sum_{i = 1}^{N_{G}} (a_{i} + b_{i} \cdot W P_{R} (i, h) + c_{i} \cdot W P_{R} {(i, h)}^{2})

(23)

Here,

G C_{Ther} (h)

is the power production cost of thermal plants at a time ‘h’. a_i, b_i, and c_i are the price coefficient of the thermal power plant. For the best act of the hybrid power system, some constraints need to be considered. The constraints for solving the OPF problem and the constraints of PHS system operation are as follows:

Constraints for OPF problem

\sum_{i = 1}^{N_{G}} G P_{Ther} (i) + W P_{R} (i) + P_{gen, EV} (i) - P L_{TL} - P_{Demand} = 0

(24)

{Vol}_{i}^{min} \leq Vo l_{i} \leq {Vol}_{i}^{max} i = 1, 2, 3 \dots N_{B}

(25)

\emptyset_{i}^{min} \leq \emptyset_{i} \leq \emptyset_{i}^{max} i = 1, 2, 3 \dots N_{B}

(26)

T L_{l} \leq {TL}_{l}^{max} l = 1, 2, 3 \dots N_{TL}

(27)

{GP}_{real, i}^{\min} \leq G P_{real, i} \leq {GP}_{real, i}^{\max} i = 1, 2, 3 \dots N_{B}

(28)

{GP}_{react, i}^{\min} \leq G P_{react, i} \leq {GP}_{react, i}^{\max} i = 1, 2, 3 \dots N_{B}

(29)

Here, $P L_{TL}$ and $P_{Demand}$ are the transmission line loss and power demand respectively. $Vo l_{i}$ & $\emptyset_{i}$ are the voltage magnitude and angle whereas ${Vol}_{i}^{min}$ , ${Vol}_{i}^{max}$ , $\emptyset_{i}^{min}$ , and $\emptyset_{i}^{max}$ are their minimum and maximum limit. ${TL}_{l}^{max}$ is the thermal limit of the transmission line. $N_{TL}$ is the number of transmission lines present in the power network. $G P_{real, i}$ & $G P_{react, i}$ are the active and reactive power generation whereas ${GP}_{real, i}^{\min}$ , ${GP}_{real, i}^{\max}$ , ${GP}_{react, i}^{\min}$ , and ${GP}_{react, i}^{\max}$ are their minimum and maximum limit.

Constraints for PHS system

P_{pump} (h) = P w_{pump, w} (h) + P w_{pump, g} (s)

(30)

P_{pump}^{\min} \leq P_{pump} (h) \leq P_{pump}^{\max}

(31)

P_{gen}^{\min} \leq P_{gen} (h) \leq P_{gen}^{\max}

(32)

E L_{PHS} (h + 1) = E L_{PHS} (h) + [(P_{pump} (h) . η_{pump}) - (P_{gen} (h) / η_{gen})]

(33)

{EL}_{PHS}^{\min} \leq E L_{PHS} (h) \leq {EL}_{PHS}^{\max}

(34)

Here, $P_{pump} (h)$ is the pumping load of the PHS system. $P_{pump}^{\max}$ , $P_{pump}^{\min}$ , $P_{gen}^{\max}$ and $P_{gen}^{\min}$ are maximum and minimum limits of the pumping mode and generating mode operation of the PHS system. $E L_{PHS} (h)$ is the energy level of the PHS upper reservoir at the time ‘h’. $η_{pump}$ and $η_{gen}$ are the PHS system efficiency for pumping mode and generating mode operations. ${EL}_{PHS}^{\max}$ and ${EL}_{PHS}^{\min}$ are the maximum and minimum energy levels of the PHS system.

Implementation of proposed technique

The proposed technique is an appropriate strategy for operating the PSH system energy level and efficient stature is expected depending on the real & predicted speed of the wind, and grid frequency. Solar-connected EVs are also integrated into the system to obtain more system profit and revenue. Six phases have been executed in the system to get the appropriate way of the utilization of the PSH system. The scheme chart in Figure 4 demonstrates the operational plan of the PSH system where P_Pump^max & P_Pump^min are the maximum & minimum limits of the pumping mode, and P_Gen^max & P_Gen^min are the maximum & minimum limits of generating mode. P_WF−g(h) is the wind power delivered to the electrical grid. There are 11 functional scenarios present through which all six scenarios can be covered as shown in Figure 4. The operation of WF-PHS based on the conditions of wind speeds is described as follows:

Figure 4.

Indicating flow-chart of the presented method.

State 1: WP_R ≥ WP_P; (f_g > 50 Hz)

The first operation of the scheme chart is shown in Figure 4 where real wind power obtained from the wind farm is more than predicted wind power. In this scenario, the excess frequency already present in the network generates additional power in the system (50 Hz is the utility frequency of India). The cost of electricity is proportionally less as the power demand is less and availability is more in the grid. The surplus power in the network can be used in an efficient method utilizing the PSH system up to the maximum energy limit in the pumping mode.

State 2: WP_R ≥ WP_P; (49.7 Hz ≤ f_g ≤ 50 Hz)

In this scenario also real wind power is more than the predicted wind power. The span of the frequency is 49.7 Hz–50 Hz in between. The cost of electricity will be comparatively less as the real wind power is more compared to the predicted wind power. The PHS system will be in operational mode in 2^nd state in this scenario. The dedicated power is directly supplied to the network and the excess electricity which is available due to the real wind speed and predicted wind speed is supplied to the PHS to operate in the pumping mode.

State 3: WP_R ≥ WP_P; (f_g< 49.7 Hz)

In this scenario, excess real wind power is available compared to the predicted one but the grid frequency is below 49.7 Hz. This implies that there is a lag of electricity in the network. So, there are three operating scenarios based on the maximum and minimum energy levels of the PHS system (EL_{PHS, max,} and EL_{PHS, min}). Moreover, they are divided instead into energy levels (EL_{PHS, opt}) and (EL_{PHS, low}).

As the level of energy reaches below EL_{PHS, low} in the PHS system, it should be taken as a risk and the usage of PHS as a turbine should only operate in an emergency to avoid system failure due to a sudden fall in frequency. When the average amount of pressure in the PHS system is observed then the energy level is less than EL_{PHS, opt,} and when the energy level is more then there is excess pressure than required. PHS system energy level is confirmed after monitoring the frequency. In stage 3 operation, real wind power will be delivered to the network to obtain appropriate frequency, if the energy level in the PHS system is below EL_PHS,low. The cost of electricity is high if sufficient power is not present in the grid as the energy level is considered to be more than EL_PHS,low but less from EL_{PHS, opt}. Further to increase the revenue in the WF-PHS system, the PHS operates as a generator to deliver power by generating a limitation to two-thirds of the maximum generation edge of the PHS system presented in scenario 4.

One more scenario is considered in which the energy level of the PHS system is comparatively more than EL_{PHS, opt}. Thus, surplus water is depleted in the PHS reservoir, and the cost of electricity is also maximum. So, the PHS system is working as a generator with maximum power-producing capacity at operative scenario 5.

State 4: WP_R < WP_P; (f_g > 49.7 Hz)

In this scenario, the predicted wind power is in excess compared to the real one and the grid frequency is more than 50 Hz. Hence, the supplied power is less than the predicted amount; surplus power is available in the grid as the range of frequency is over 50 Hz. So, there is no requirement of operating the PHS as a generator to supply the predicted power as it may maximize the grid frequency. The PHS system will work in pumping mode to store the energy and balance the frequency to its base value of 50 Hz. Based on the energy level of PHS, two operating techniques have been considered for two different pumping limits. The pumping limit of PHS is arranged to the maximum value (scenario 8), if the energy level is below EL_PHS,opt then pumping will operate two-thirds of the determined pumping border (scenario 9). WF provides the electricity for driving the pumping mode other than purchasing it from the grid for both scenarios as wind power is cost-effective. After fulfilling the power requirements, surplus electricity is sold to the grid.

State 5: WP_R < WP_P; (49.7 Hz ≤ f_g≤ 50 Hz)

In the scenario, predictive wind power is in excess compared to the real and the range of grid frequency is 49.7 to 50 Hz. In this condition, the PHS operates as a generator to ease the uncertainties between the real and predicted power. In scenario 8 the energy level is concentrated in the PHS system for these two operative strategies are advised. The PHS system will perform as a generator, if the energy level of PHS is below EL_{PHS, opt} and it supplies lesser power due to the real and predicted wind power difference (as in scenario 10), or the PHS system will work as a generator mode with regulating its maximum outcome to PHS generation and the lowest value is arranged to the mismatch among the predicted and real wind power of PHS (in operative scenario 11).

State 6: WP_R < WP_P; (f_g < 49.7 Hz)

The predicted wind power is more compared to the real wind power and the grid frequency is below 49.5 Hz in this condition. Since the grid frequency is below 49.5 Hz, there will be a huge spike in the electricity demand, and the thermal-wind farm hybrid system is unable to fulfill the excess requirement. So, the PHS system will start acting in generating mode. PHS system works in generating mode when the energy level is lower than EL_{PHS, low}, to mitigate the lagging power which occurs due to the difference between the real and predicted power (as in scenario 6). The PHS system has the desire to arrange a minimum level of generated power when the energy level is more than EL_{PHS, low}. Moreover, the maximum level of power generation is arranged to the maximum generating capacity (scenario 7).

The optimal operation strategy of the PHS system and EV batteries are shown in Figure 4. The success and effectiveness of the presented method depend on the energy level scheduling of the PHS and EV batteries.

The step-by-step process of the execution method is as follows:

Step 1: Consider system data (modified IEEE 30-bus system).

Step 2: Collect hourly real and predicted wind speed data.

Step 3: Calculate the wind power generation and wind power cost.

Step 4: Calculate the imbalance price considering wind speed data and compare profit with and without imbalance price.

Step 5: Try to minimize the adverse effect of imbalance price in the system economy by optimal operation of renewable energy sources and storage devices.

Step 6: Calculate the system profit and revenue by maintaining the grid frequency by appropriate operating of the energy level of the PHS upper reservoir and solar-connected EV battery storage in the wind-associated system.

Results and discussions

This work has been performed in a restructured electricity environment. Now the entire world is going toward a deregulated environment due to the enhancement in the financial benefit of the energy consumer. This situation arises only because of the huge competition among all market participants. A modified IEEE 30-bus test system has been chosen to validate the presented approach. In the considered test system, bus no. 1 is selected as the reference bus and the system MVA limit is 100 MVA. It has nineteen loads, six generators, and 41 transmission lines.³¹

The considered wind farm consists of 30 wind turbines and each having capacity of 3 MW, so a total capacity of 90 MW wind farms have been chosen. In this work, the effect of imbalance prices on the system economy and their effect reduction methodology has been suggested using storage devices. The wind-incorporated restructured power system has worked based on real-time wind speed data. So, real-time data for real and predicted wind speed has been considered here to verify the presented approach. At first, hourly real and predicted wind speed data are collected for a day (shown in Figure 5). The wind speed data has been taken for a randomly chosen place i.e., Vijayawada, India.³²

Figure 5.

Real and predicted wind speed for a day.

The predicted wind speed data for March 20^th, 2023 is taken on March 15^th, 2023 and the real wind speed data for March 20^th, 2023 is taken on March 21^st, 2023. From the gathered real and predicted wind speed data it is observed that there is a disparity between real and predicted wind speeds in most hours. This happened due to the changeable behavior of wind flow. In a renewable associated power system, the renewable power generating stations need to provide the power generation data for the future operation to the system controller and based on the submitted bid, the system controller arranged the power system operation. In a general scenario, the renewable power plant submits the bid data based on the prediction of renewable sources. If any divergence has occurred between the predicted and real data then the power market controller imposes a penalty or gives rewards to the renewable power station for their less or extra power supply to the electricity grid. In a renewable integrated network, grid frequency control is a vital aspect as it encourages the system's stability and safety. The normal operating frequency of the Indian power grid is 50 Hz and the tolerable frequency range is 49.5 Hz–50.2 Hz. Figure 6 portrays a grid frequency scenario for Vijayawada, India on March 15^th, 2023 to verify the efficiency of the proposed method.

Figure 6.

Grid frequency for a day (in Hz.).

The power system's profit depends on generation cost and system revenue. The system revenue of the power network is determined by the generated power and location-based marginal price (Equation 8) of that specific location. The main motto of this work is to exploit the system profit and revenue by maintaining the grid frequency by appropriate operating of the energy level of the PHS upper reservoir and EV battery storage in a wind-associated system. The impact of the imbalance price is very crucial for the profit maximization of a system. This work has been performed in the deregulated system environment. Several steps are involved to conduct the proposed work which are depicted as:

Case 1: System economic performance with the presence of an imbalance price.

Case 2: System economic performance with proposed method phase-1.

Case 3: System economic performance with proposed method phase-2.

Initially, the wind farms are placed at bus no. 4 in the considered system. Bus no. 4 contains the maximum energy demand in the system. So, the placement of the wind farm in that bus provides maximum impact on the system. The boosting of the system profit is expanded by sustaining and governing the grid frequency and the suitable arrangement of the energy level of PHS and EV battery storage devices. The considered energy level of the PHS upper reservoir and EV battery banks are shown in Table 1. The efficiency of the PHS system in pumping and generating modes has been considered as 80% and 85%, respectively.

Table 1.

Considered energy level of PHS upper reservoir and EV battery.

PHS Upper Reservoir
EL_PHS,max	EL_{PHS, Opt}	EL_{PHS, low}	EL_{PHS, min}	EL_PHS,ini
90 MWh	50 MWh	25 MWh	15 MWh	35 MWh
EV Battery Bank
EL_EV,max	EL_{EV, Opt}	EL_{EV, low}	EL_{EV, min}	EL_EV,ini
1.8 MWh	0.9 MWh	0.45 MWh	0.2 MWh	1.8 MWh

Initially, some energy is required to start the operation of the PHS system and EV battery system. Here, the initial energy level of the PHS is considered 35 MWh whereas it is 1.8 MWh for the EV battery system. The proper sharing of the storage device's energy level is very crucial as the charging/discharging/idle mode operation depends on the energy level as well as based on the physical conditions of power availability and grid frequency.

Case 1: System economic performance with the presence of an imbalance price

In this section of the work, the system profit and system revenue have been measured by considering the imbalance price. To perform this, the optimal power flow problem has been solved hourly basis with the placement of wind farms with different capacities in the system. Figure 7 displays the generated wind power capacity considering the real and predicted wind speeds. From Figure 7 it is seen that in maximum hours, there is a gap between the real and predicted wind power. So, an imbalance price scenario will be present in the system. The system profit and revenue in the presence of an imbalance price are calculated by assimilating the wind farm into the present thermal power plant. The system economic parameters for the considered power system are displayed in Table 2. From the result, it is seen that the profit is minimized in the case when the imbalance price value is maximum and this is also in the “−ve” domain, which indicates the penalty imposed by the ISO on the GENCOs.

Figure 7.

Generated wind power based on real and predicted wind speed (in MW).

Table 2.

Imbalance price and profit of WF-thermal hybrid systems.

Hour	Revenue ($/h)	Imbalance Price ($/h)	Profit ($/h)	Hour	Revenue ($/h)	Imbalance Price ($/h)	Profit ($/h)
1	12,631.39	−2476.5	−77.8826	13	12,436.13	−1392	1324.27
2	12,436.13	0	2716.27	14	12,172.12	45.5633	3163.461
3	12,257.6	62.84	3054.642	15	12,333.2	53.0887	2930.457
4	12,172.12	53.7347	3171.633	16	12,257.6	62.8446	3054.646
5	12,172.12	19.67	3137.568	17	12,631.39	−1623.2	775.4174
6	12,172.12	32.6725	3150.571	18	12,704.92	−2899	−625.143
7	12,631.39	−561.05	1837.567	19	12,172.12	0	3117.898
8	12,529.26	25.344	2592.159	20	12,172.12	53.73	3171.628
9	12,704.92	−2446.6	−172.743	21	12,172.12	0	3117.898
10	12,631.39	−2.4765	2396.141	22	12,333.2	40.2274	2917.596
11	12,172.12	0	3117.898	23	12,436.13	0	2716.27
12	12,257.6	18.17	3009.972	24	12,704.92	−2899	−625.143

The overall system profit depends on the system generation cost and revenue. In a wind-associated power system, the system revenue has two sections i.e., revenue from the thermal power plant and revenue from the wind firm. Due to the high generation capacity of thermal power in the system as compared to wind power, the revenue is also high from the thermal power plant. Figure 8 shows the percentage contribution of thermal and wind power revenue in the combined system. It has been seen that the revenue of wind power is maximum for hours 4, 5, 6, 11, 14, 19, 20, & 21 due to the highest wind power integration in the system at that period.

Figure 8.

Contribution of thermal and wind farm revenue (in %).

Case 2: system economic performance with proposed method phase-1

In this case, the proposed approach phase 1 has been implemented for maximizing the system revenue, and profit in the presence of an imbalance price with maintaining the frequency of the electricity grid. Here, the operating control of the energy level of the upper reservoir of the PHS system provides optimal results. A proportional study has been directed for the WF-PHS hybrid power system with the overall revenue, system generation cost, profit, and energy level of the PHS reservoir which is displayed in Table 3. From Tables 2 and 3, it has been seen that the system economic parameters i.e., revenue and profit have boosted in the most hours after the implementation of the proposed method phase 1. The impact of the imbalance prices is also included in Table 3. It is found that, if the consequence of the imbalance price is not existing in the system then the system profit will become much more.

Table 3.

Comparison of revenue & profit of the hybrid system (proposed method phase-1).

Hr.	Power Generation (MW)		Final Energy Level of PSP (MWh)	Revenue ($/h)					Total System Gen. Cost ($/h)	Profit ($/h)
Hr.	From Real Wind Speed	From Predicted Wind Speed	Final Energy Level of PSP (MWh)	Thermal Plant Revenue	Revenue from PHS Gen. Operation	Wind Farm Revenue	Revenue from PHS-WF Hybrid System	Total System Revenue from Thermal-PHS-WF Hybrid System	Total System Gen. Cost ($/h)	Profit ($/h)
1	53.25	90	23.23	11,404.58	437.15	2352.88	1606.92	13,011.51	10,551.97	2459.52
2	69	69	31.23	11,854.09	0	3029.77	2587.05	14,441.14	10,987.46	3453.68
3	83.25	47.25	39.23	11,821.17	0	3201.33	3953.84	15,775.01	11,020.01	4755.01
4	90	61.5	47.23	11,767.89	0	3470.53	3887.35	15,655.24	11,018.56	4636.68
5	90	83.25	55.23	11,741.45	0	3598.63	3185.87	14,927.33	11,008.48	3918.85
6	90	77.25	43.47	11,044.81	425.1	3791.79	5194.22	16,239.03	10,532.75	5706.28
7	53.25	61.5	31.71	11,404.59	437.14	2352.88	2867.42	14,272.02	10,551.97	3720.04
8	61.5	53.25	38.1	11,990.22	0	2398.82	2068.82	14,059.04	10,988.76	3070.28
9	47.25	83.25	46.31	12,339.47	0	1731.06	−795.43	11,544.04	11,176.39	367.64
10	53.25	90	34.54	11,404.58	437.15	2352.88	1606.92	13,011.51	10,551.97	2459.52
11	90	90	26.69	11,082.14	284.83	3804.13	4376.45	15,458.61	10,540.08	4918.52
12	83.25	77.25	34.69	11,789.44	0	3361.37	2945.08	14,734.53	11,003.19	3731.33
13	69	90	16.46	11,771.33	606.4	3334.85	3748.91	15,520.25	10,939.81	4580.44
14	90	69	24.46	11,767.66	0	3470.53	3556.21	15,323.87	11,018.56	4305.31
15	77.25	53.25	32.46	11,876.12	0	2960.71	3183.91	15,060.04	11,027.92	4032.11
16	83.25	47.25	40.46	11,821.17	0	3201.33	3953.84	15,775.01	11,020.01	4755.01
17	53.25	77.25	48.46	12,246.48	0	1987.69	28.14	12,274.62	11,131.79	1142.82
18	47.25	90	36.69	11,462.99	439.21	2104.61	1088.28	12,551.24	10,577.83	1973.41
19	90	90	36.69	11,155.21	0	3828.91	3828.89	14,984.09	10,558.58	4425.51
20	90	61.5	28.85	11,082.14	284.83	3804.13	5605.47	16,687.62	10,540.08	6147.54
21	90	90	36.85	11,686.36	0	3861.61	3423.99	15,110.36	10,993.37	4116.99
22	77.25	61.5	29.01	11,237.83	288.34	3327.44	4594.78	15,832.61	10,520.54	5312.07
23	69	69	29.01	11,351.26	0	3012.97	3012.97	14,364.23	10,547.11	3817.12
24	47.25	90	17.24	11,462.99	439.21	2104.61	1088.25	12,551.24	10,577.83	1973.41

In the proposed process, the energy level of the PHS upper reservoir has been allocated into four portions. The charging/discharging/idle operation of the PHS system has been accomplished based on the real-time condition of real and predicted wind power with grid frequency. The action of the PHS system in generating/ pumping mode with the energy level allocation for the said operation, all these factors have been recognized using the proposed technique. The optimum power flow delivers the best generation pricing by completing the generation re-scheduling process in a thermal plant. Figure 9 displays the generated thermal power and the matching location-based marginal price for all generators after the implementation of the proposed approach in the hybrid network.

Figure 9.

(a) generated thermal power (MW) and (b) location-based marginal price ($/MWh) of the hybrid system (proposed approach).

The appropriate exploitation of the PHS upper reservoir energy level is very significant for the financial and steady action of the hybrid system. The effective and working range of power for the generator and pumping mode operation of the PHS system after the application of the proposed method is revealed in Figure 10. The range between [ − 10, 10] represents the power requirement from the grid or power supply to the grid in MW at the generating mode and pumping mode operation of the PHS plant. This operating range is controlled by three parameters: (a) present wind power availability status, (b) present grid frequency status, and (c) present energy status of the PHS plant. The power is zero at the 19^th and 23^rd hour, which denotes that the PHS stays in an idle state.

Figure 10.

Operating range of PHS system (in MW).

In the wind-associated competitive electrical system, the power delivery contract has been signed between the GENCOs and DISCOs depending on the predicted wind speed. Based on the real and predicted wind power scenario, the PHS system is operating in different tactics either in generator or pump or ideal mode to uphold the bidding power. Since the proposed method is accomplished in a deregulated atmosphere, the mixing of the WF-PHS system will enhance socio-economic comfort. Wind power first fulfills the local system's energy demand and then the remaining power is delivered to the grid. So, there is a reduction in transmission line losses as well as a drop in system congestion. This part performs the comparative studies of system economic factors between the existing²⁹ and proposed methods. There were some restrictions in the operation of the energy storage device's energy level in the existing method which has been declined in the proposed method. Figure 11 illustrates the differentiation between the projected and existing logic phase-1 revenue for WF-PHS systems respectively. The revenue earned by the PHS system has been separated into two parts i.e., pumping cost and generator revenue. The pumping cost can be appraised as “−ve” revenue due to the price needed to be delivered to the electrical grid for captivating the required power from the grid to complete the pumping mode process. The generating mode of the PHS system delivers the power to the grid for selling to the customer which provides positive revenue. So, it is evident that the PHS operation in pumping mode can put extra economic trouble on the system economy. So, suitable operation of the energy level can only offer enhanced financial sustainability of the power system. Figure 12 displays the comparative studies of thermal-WF-PHS hybrid system revenue whereas Figure 13 shows the comparative analysis of system profits. The system profit is directly related to the revenue and imbalance prices, so the curves are different due to the presence of imbalance prices during some hours. From Figures 11–13, it has been observed that the proposed method phase-1 gives superior results in all financial aspects as differentiated from the existing method.

Figure 11.

Comparative studies of WF-PHS hybrid system revenue ($/h).

Figure 12.

Comparative studies of thermal-WF-PHS hybrid system revenue ($/h).

Figure 13.

Comparative studies of thermal-WF-PHS hybrid system profit ($/h).

In this work, the PHS upper reservoir energy level played the main role in Phase-1 operation. The prime innovation of this work is the arrangement of the PHS energy level for preserving the grid frequency in presence of the wind vagueness. Figure 14 displays the energy level evaluation between the proposed method phase-1 and the existing method for the restructured power environment. From the results, it has been seen that the operation of the energy level in the proposed method phase-1 is competent and this method is never recommended for charging the PHS system to the maximum energy level in the maximum time. So, it can be concluded that the proposed method phase-1 is finer and more proficient for grid frequency control by the most favorable operation of the WF-PHS hybrid system.

Figure 14.

Comparative studies of PHS reservoir energy level (MWh).

Case 3: system economic performance with proposed method phase-2

Now a day electric vehicle is most popular for their environment-friendly features and low running & maintenance cost. Electric vehicles can be used in electrical power systems as energy storage devices. The main component of electrical vehicles is the battery bank. This battery bank can be used in the charging and discharging mode as per the power requirement of the electrical network. In the peak load condition, the EV can operate in the discharging mode and supply additional power to the grid to remove the power deficiency burden and enhance the security and stability of the grid. On the other hand, the battery is operating in the charging mode at the time of off-load conditions. There are different types of batteries used in electric vehicles i.e., Li-ion, lead acid, supercapacitors, etc. In this work, the Li-ion batteries have been used to enhance the system economic parameters of a WF-PHS-EV hybrid system. It is assumed that the EVs are connected to solar photovoltaic systems. So, charging and discharging of the EV batteries are primarily performed by the solar PV system.

Table 4 depicts the details of the battery bank used in the EV system. The solar power capacity of the chosen places (i.e., Vijayawada, India) has been collected for March 20^th, 2023 to verify the presented method in the real-time scenario. The energy level of the EV battery bank has divided into four parts. The operation of the EV batteries has been performed using the proposed method phase-2 shown in Figure 4 to enhance the system profit further as compared to the proposed approach phase-1 and the existing method.

Table 4.

Details about the EV battery storage system.

Sl. No.	Type of Battery	Power Rating (kW)	Efficiency (%)	Single Battery Rating (kW)	Total no. of battery
1	Li-ion	0–100	90	60 kW	30

The solar-connected EV system also has inverters. The efficiency of the batteries and inverters have been considered as 0.9 and 0.92, respectively. The optimal and effective operation of the EV battery storage system has been displayed in Table 5. From these results, it can see that the in some cases the battery is working in the charging mode, and in some cases, the battery is operating in discharging mode based on the existing energy levels of the battery bank and the present scenarios of the grid frequency along with the real & predicted wind speed situations.

Table 5.

Revenue & profit of hybrid power plant (by proposed approach phase-2).

Hr.	Operation State	Solar Power (MW)	Solar Power to Grid (MW)	Starting Energy Level of Battery (MWh)	Battery Power to Grid (Sending) (MW)	Battery Power to Grid (Actual) (MW)	Battery Charging/ Discharging	Charging Power Requirement (Solar To Battery) (MW)	Final Energy Level of Battery (MWh)
1	5	0	0	1.8	0	0	—	0	1.8
2	1	0	0	1.8	0	0	—	0	1.8
3	2	0	0	1.8	0	0	—	0	1.8
4	2	0	0	1.8	0	0	—	0	1.8
5	1	0	0	1.8	0	0	—	0	1.8
6	3	0	0	1.8	0.9	0.7452	Discharging	0	0.9
7	6	0.09	0.09	0.9	0	0	—	0	0.9
8	2	0.44	0.44	0.9	0	0	—	0	0.9
9	4	0.68	0.68	0.9	0	0	—	0	0.9
10	5	0.67	0.67	0.9	0	0	—	0	0.9
11	3	1.01	1.01	0.9	0	0	—	0	0.9
12	1	1.68	0.593	0.9	0	0	Charging	1.087	1.8
13	6	1.63	1.63	1.8	1.35	1.1178	Discharging	0	0.45
14	2	1.54	1	0.45	0	0	Charging	0.543	0.9
15	2	1.84	1.84	0.9	0	0	—	0	0.9
16	2	1.7	1.7	0.9	0	0	—	0	0.9
17	4	0.45	0.45	0.9	0	0	—	0	0.9
18	5	0.29	0.29	0.9	0	0	—	0	0.9
19	2	0.02	0.02	0.9	0	0	—	0	0.9
20	3	0	0	0.9	0	0	—	0	0.9
21	1	0	0	0.9	0	0	Charging	1.087	1.8
22	3	0	0	1.8	0	0	—	0	1.8
23	2	0	0	1.8	0	0	—	0	1.8
24	5	0	0	1.8	1.35	1.1178	Discharging	0	0.45

The comparative studies of the existing & proposed approach phase-1 and phase-2 regarding the system profit have displayed in Table 6 and Figure 15. It has been seen that the proper operation of the PHS system is sufficient enough to get more revenue and profit in the proposed method phase-1 as compared to the existing one. But, the proposed method phase-2 was also implemented to enhance the system's economic parameters once again by the operation of PHS and solar-connected EV system in a wind-associated deregulated power network. From Figure 15 it has seen that the system profit was 87,369.78651$ for the existing method for a day whereas this has reached 89,779.20314$ and 89,905.20314$ for the proposed method phase-1 and phase-2, respectively. These results have proven the effectiveness and efficiency of the presented method.

Figure 15.

System profit with existing & proposed approach phase-2 (in $/day).

Table 6.

System profit with existing & proposed approach phase-1 and phase-2 (in $/h).

Hour	Proposed Method (Phase-1)	Proposed Method (Phase-2)	Existing Method²⁹	Hour	Proposed Method (Phase-1)	Proposed Method (Phase-2)	Existing Method²⁹
1	2459.529	2459.5291	2459.529	13	4580.443	4580.4432	4552.097
2	3453.684	3453.6843	3453.684	14	4305.316	4326.3164	4305.543
3	4755.009	4755.009	4755.009	15	4032.12	4049.1196	4032.12
4	4636.686	4636.6859	4636.686	16	4755.009	4755.009	4755.009
5	3918.85	3918.8504	3918.85	17	1142.828	1142.8275	1142.828
6	5706.285	5706.2845	4979.536	18	1973.412	1973.4121	1973.412
7	3720.046	3720.0456	3573.842	19	4425.52	4425.5199	4425.52
8	3070.28	3070.2803	3070.28	20	6147.545	6147.545	5663.91
9	367.6475	367.64753	367.6475	21	4116.991	4116.991	4116.991
10	2459.529	2459.5291	2459.529	22	5312.071	5312.0712	4780.37
11	4918.527	4968.5274	4425.52	23	3817.128	3817.1276	3817.128
12	3731.335	3769.3355	3731.335	24	1973.412	1973.4121	1973.412

Comparative studies among different optimization techniques

For checking the efficiency and effectivity of the proposed method, three meta-heuristic algorithms i.e., Slime Mould Algorithm (SMA),³¹ Artificial Gorilla Troops Optimizer (AGTO),³¹ and Moth–Flame Optimization Algorithm (MFO)³³ have been used in this work for comparative studies along with the MiPower solver. The system profit comparison for the considered day with all four types of optimization process has been depicted in Figure 16. From the comparative studies it has been seen that the AGTO techniques provide the highest profit whereas the SMA and MFO also give the better profit as compared to the existing method.

Figure 16.

System profit comparison with several optimization techniques (in $/day)

In this work, the PHS reservoir and EV battery energy level played the main role to fulfill the considered objectives. The novelty of this work is the scheduling of the energy level of storage devices for keeping the grid frequency in presence of the wind uncertainty. From the results, it can conclude that the proposed method is superior to the existing method to fulfill the customer's economic requirement and maintain the system’s safety and security.

Conclusions

A systematized approach has been proposed to increase the system profit and revenue of the thermal-WF-PHS-solar connected EV incorporated hybrid system in the deregulated power market. The frequency of the grid is also maintained by optimal scheduling of the storage device's energy level. The existing PHS system's operation is completely dissimilar from the proposed PHS systems. In the existing scheme, the handling of the energy level of the storage system is restricted. It is used only for crucial savings in the peak demand hours. But the proposed PHS system is advised to drive to pay off for the deficit power that occurs due to the changeable wind power nature to fulfill the commitment power. The PHS system in this work is not only operated to compensate for the power difference between real and predicted wind power but to increase the system profit in the hybrid system. In phase-1 of the proposed method PHS system was operated as the storage device whereas in phase-2 solar connected EVs have been used along with the PHS system to extend the economic sustainability of the system. The result of the proposed method phase-1 and phase-2 is compared with the existing one. From the outcome, it can be understood that the proposed approach offers more desirable results for all cases. From the detailed studies of the results, it can conclude that the PHS reservoir and EV battery are used very well in the proposed approach and for this reason, the revenue and profits are also obtained well.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article

ORCID iD

Jayendra Kumar

Shreya Shree Das received the MTech degree in Power And Energy Systems Engineering from Mizoram University, India, in 2022, with a Gold Medallist. She is pursuing her PhD research involving renewable energy and power engineering from the School of Electronics Engineering at VIT-AP University, India. Her current research includes Power System Economics, Renewable Energy Integration, Congestion Management, Smart Grid, Electricity Market, Energy Management, and Machine learning. She has already published many research papers in International Journals (IET, Elsevier, Springer, T&F, etc.) and Conferences in the field of power systems and congestion management.

Jayendra Kumar is currently working as an Associate Professor in the Department of Electronics and Communication Engineering, Vellore Institute of Technology, Amaravati. He received a Bachelor of Engineering (BE) and Master of Engineering (ME) from Solapur University, Maharashtra, India in 2011 and 2014 respectively and was awarded a PhD at the National Institute of Technology Silchar, Assam, India in 2018. He is currently with the Frequency reconfigurable, reduced surface waves (RSWs) and high gain single element antennas. Before this, he was working on electronic bandgap structures (EBGs), ultra-wideband antennas and mutual coupling reduction in multiple-input multiple-output (MIMO) antennas. His research interests are dielectric resonator antennas, metamaterials/metasurface, conformal antennas, multilayered substrates, and Higher-order modes antennas. He has delivered guest lectures and short term training programs (STTPs) on microstrip antennas, high-frequency structural simulators (HFSS), Matlab and solar energy awareness programs. He is a member of the IEEE Antennas and Waves Propagation Society (APS), IEEE Microwave Theory and Techniques Society, a lifetime Indian Society of technical education (ISTE) and a reviewer of IEEE Transaction on Vehicular Technology, IEEE Communication Magazine, International Journal of Systems, Control, and Communications (IJSCC), AEU - International Journal of Electronics and Communications, SN Applied Sciences, and Computer and Electrical Engineering Journal.

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Socio-economic advancement and grid-frequency control of a wind farm-PHS-solar connected EV hybrid electrical system considering imbalance prices

Abstract

Keywords

Introduction

System modeling

Wind farm-based power generation model

Pump hydro storage power model

Solar-connected electric vehicle modeling

Objective function

Constraints for OPF problem

Constraints for PHS system

Implementation of proposed technique

State 1: WPR ≥ WPP; (fg > 50 Hz)

State 2: WPR ≥ WPP; (49.7 Hz ≤ fg ≤ 50 Hz)

State 3: WPR ≥ WPP; (fg< 49.7 Hz)

State 4: WPR < WPP; (fg > 49.7 Hz)

State 5: WPR < WPP; (49.7 Hz ≤ fg≤ 50 Hz)

State 6: WPR < WPP; (fg < 49.7 Hz)

Results and discussions

Case 1: System economic performance with the presence of an imbalance price

Case 2: system economic performance with proposed method phase-1

Case 3: system economic performance with proposed method phase-2

Comparative studies among different optimization techniques

Conclusions

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References

State 1: WP_R ≥ WP_P; (f_g > 50 Hz)

State 2: WP_R ≥ WP_P; (49.7 Hz ≤ f_g ≤ 50 Hz)

State 3: WP_R ≥ WP_P; (f_g< 49.7 Hz)

State 4: WP_R < WP_P; (f_g > 49.7 Hz)

State 5: WP_R < WP_P; (49.7 Hz ≤ f_g≤ 50 Hz)

State 6: WP_R < WP_P; (f_g < 49.7 Hz)