The low-carbon economic operation strategy of virtual power plant under different electricity-gas-heat-carbon multi-market synergy scenarios

Abstract

Studying the grid integration of renewable energy power generation is crucial for achieving the goal of carbon neutrality since it may have a significant influence on the secure and reliable functioning of the power system. In order to solve the problem of deviation impact caused by renewable energy fluctuations and the optimal scheduling of VPP (Virtual Power Plant), the study divides the internal aggregation unit of the virtual power plant into two parts to model. One part is the source equipment, including wind power generation equipment, gas turbine, gas boiler and waste heat boiler. And the other part is the generalized Energy storage, including electric vehicles, air conditioners and alternative response loads. Ultimately, a generalized energy storage-based virtual power plant operation optimization model is developed under multi-market coordination of electricity-gas-heat-carbon. According to the study’s findings, adding more power-to-gas technology boosts revenue in the carbon trading market by 25.24 percent. The energy market’s revenue is equal to that in the absence of a carbon trading market, and the income of the natural gas market increases by $ 32.96. The income of the carbon trading market is $ 181.51, and the final operating cost is reduced by $ 180.80, a drop of 7.81%. To sum up, the suggested approach may more effectively achieve the best distribution of different energy sources, increase the dependability of VPP operation, and make it more low-carbon.

Keywords

Virtual power plant cogeneration of electricity heat and gas generalized energy storage column constraint generation low carbon dispatch optimization

1. Introduction

In today’s society, the whole world is vigorously promoting the industrialization, resulting in increasingly bad ecological environment and shortage of traditional fossil energy. The world is in a critical transition period of replacing traditional fossil energy with renewable energy. Low pollution, sustainability, and environmental friendliness are benefits of renewable energy sources like solar and wind energy. Additionally, nations all around the world are aggressively supporting renewable energy producing technologies like photovoltaics and wind power [1]. In the past, the old power system used a large-scale power distribution method, which resulted in significant expense and power loss during transmission [2]. Since the development of distributed generation of renewable energy, it is utilized extensively in power grids and has the qualities of minimal pollution, cost savings, and excellent transmission efficiency. The electrical grid’s ability to operate steadily and safely will be jeopardized by operational irregularities [3]. The Virtual Power Plant (VPP) can make better use of the power market to increase the flexibility and cost-effectiveness of energy dispatching on the demand side. And it can coordinate and interact between the supply and demand sides of the power system. In addition, large-scale renewable energy integration into the grid has been facilitated by the advancement of energy storage technologies. Among them, a generalized energy storage system can quantify the flexible load response capability, which makes the generalized energy storage operation optimization strategy more rapid [4]. The research aims to ensure the reliability and stability of power grid operation, suppress the functional deviation of VPP caused by the uncertainty of wind and rain, reduce the optimal operation cost of VPP. And the operation fluctuation caused by industrial energy transformation and renewable energy can be solved. Therefore, the research comprehensively considers the synergy of virtual energy storage and electric energy storage, and uses electric to gas equipment to build the VPP operation optimization model under the coordination of electricity, heat, gas and carbon multi-market. Finally it arranges the VPP model based on generalized energy storage to ensure low carbon and high income in multi-market transactions.

2. Related work

In today’s society, countries around the world apply VPP projects to ensure the reliable grid connection of uncertain and strong renewable energy. And by aggregating different distributed equipment, the external stable and reliable power supply is realized. So it can realize the direct participation of renewable energy in the power market [5]. In addition, energy storage in a broad sense refers to all equipment and measures that can change the spatiotemporal characteristics of electric energy and play a buffer role in electricity’s supply and demand. And energy storage can further deal with the unpredictability of renewable sources of energy like sunlight and wind [6]. Many scholars have conducted in-depth discussions on VPP in generalized energy storage. To more effectively regulate the distributed generating system’s indirectness, Alahyari et al. [7] designed a VPP quotation strategy method including flexible demand, energy storage, and wind power production, and the success rate of the plan was verified by experiments. In order to assess the potential of VPP urban development, Wang et al. [8] proposed the concept of VPP to offer the flexibility that the renewable energy system requires and to choose the appropriate grid services. Finally a multi-market collaborative optimization model based on VPP was proposed. Experimental results show that the model takes full advantage of the flexibility of DER and provides results that can serve as a blueprint for more VPP applications. To support the presence of small-scale renewable energy producers in the market, Chang et al. [9] introduced the concept of VPP to apply energy storage system together with microturbine-based generators to reduce the variability of output. In order to ensure that the generation of VPP keeps the market contract value steady, VPP adopts a new centralized feeder flow control (FFC) method. Comparing it with the traditional method, the research results verified the effectiveness of the method. The VPP bidding strategy used in the Spanish power market was optimized by using a multi-stage stochastic programming technique by this technique. This technique was created by Wozabal and Rameseder [10] to address the issue of variable wind energy output and stochastic electricity price. Extensive sample comparisons were carried out to confirm that the stochastic program obtained the best Optimal strategies are significantly better than deterministic programming. In order to solve the economic problems of fluctuations in electricity prices and rapid rises, Naval et al. [11] developed a new VPP design that incorporates every full-scale distributed renewable energy production technology currently on the market. The experimental findings of the VPP model showed the significance of technical and economic management of all production facilities to minimize grid reliance and cost when the model was applied to real data of an irrigation system. For addressing the issue of collective user day-ahead energy management with intermittent renewable energy generation and unknown market pricing, Yin et al. [12] created a two-stage robust Stackelberg game model. It was engaged in day-ahead and real-time market transactions, and ran as a VPP. The simulation findings supported the model’s logicalness and efficacy. The energy management service market’s VPP bidding technique was examined by Nguyen-Duc and Nguyen-Hong [13]. They also created a two-stage robust optimization model and used it to optimize tiny VPPs. The outcomes confirmed the technique’s efficacy and increased VPPs’ earnings. In order to explore the basic quantization properties related to charge transport and storage, Bueno and Davis [14] used a method that spans nanoelectronics, electrochemistry and derived nanosensing to connect. During this time, they analyzed and verified of the electrochemical characteristics of dry and wet electrons in the connection of charge electrokinetics. This was consistent with the generalized energy storage theoretical framework for electron transfer rates.

Based on the above-mentioned analysis of research on VPP and generalized energy storage, relatively mature results had been achieved in their respective fields. But there were few research contents that comprehensively considered the combination of distributed energy and generalized energy storage as well as the operation optimization in multiple markets. Most studies rarely designed carbon trading markets. The aforementioned issues are resolved by a VPP model based on generalized energy storage and its operation optimization under the multi-market coordination of electricity-heat-gas-carbon are studied and constructed. The innovation of the research lies in the comprehensive consideration of the system function of virtual energy storage and electric energy storage. So the two can work together under the generalized energy storage framework. In addition, the stabilizing effect of the generalized energy storage on the uncertainty of renewable energy such as the peak-shaving and valley filling effect on load fluctuation were fully considered. And the processing strategies of the virtual energy storage and electric energy storage in the generalized energy storage were fully studied in the case of separate access and simultaneous access.

3. Building a virtual power plant operation optimization model based on universal energy storage

3.1 Construction of the virtual power plant’s source layer equipment model

Due to the expansion of globalization, environmental pollution and energy shortages are becoming more and more serious, and countries around the world urgently need to use renewable energy to survive the critical transition period. VPP can use digital technology to achieve rapid transformation. It is a distributed power plant based on cloud technology. Through high-level software architecture, digital communication, control and other technologies can be used to realize the limited aggregation of various distributed equipment. For example, it includes methods for allocating energy and energy storage, realizing energy exchange, and sharing and optimizing configuration [15]. Its overall structure operates as shown in Fig. 1.

Figure 1.

Virtual power plant operation structure.

At present, China is vigorously developing wind power generation. Models for wind power generation most frequently employ the Doubly Fed Induction Generator (DFIG) [16]. The wind turbine begins to produce energy when the wind speed exceeds the cut-in wind speed, allowing it to attain the rated power, which is the rated wind speed. And if the wind speed does not satisfy the parameters, it is the cut-out wind speed. See Eq. (1) for the relationship between wind turbine forecast output $P_{\textit{winp}}$ and wind speed forecast value $v_{w}$ .

$\displaystyle P_{\textit{winp}}=\left\{{\begin{array}[]{l}0,\nu_{w}<v_{\textit% {ciws}},v_{w}>v_{\textit{cows}}\\ \frac{v_{w}^{3}-v_{\textit{ciws}}^{3}}{v_{\textit{rws}}^{3}-v_{\textit{ciws}}^% {3}},v_{\textit{ciws}}<{v}_{w}<v_{\textit{rws}}\\ P_{r},v_{\textit{rws}}<v_{w}<v_{\textit{cows}}\\ \end{array}}\right.$ (1)

In Eq. (1), $v_{\textit{ciws}}$ , $v_{\textit{cows}}$ , $v_{\textit{rws}}$ are respectively the wind turbine generator’s cut-in, cut-out, and rated wind speed, which correspond to the wind turbine’s rated output power. After obtaining the Weibull distribution parameters of wind frequency at a certain height using the two-parameter Weibull parameter, the Weibull distribution parameters of wind frequency at different heights can be obtained. These parameters can be used to accurately predict wind power generation. The probability distribution density function is shown in Eq. (2).

$\displaystyle f\left({v_{w,h}}\right)=\frac{\zeta_{w,h}}{\psi_{w,h}}\left({% \frac{v_{w,h}}{\psi_{w,h}}}\right)^{\zeta-1}\cdot\exp\left[{-\left({\frac{v_{w% ,h}}{\psi_{w,h}}}\right)^{\zeta}}\right]$ (2)

In Eq. (2), $\zeta_{w,h}$ represents the Weibull shape parameter, $\psi_{w,h}$ represents the distribution parameter, and $v_{w,h}$ is the height-specific wind speed. Then the expected value and variance $\sigma\left({v_{w,h}}\right)$ expression of wind speed Weibull can be obtained as Eq. (3).

$\displaystyle\left\{{\begin{array}[]{l}\mu\left({v_{w,h}}\right)=\psi_{w,h}% \cdot\vartheta\left({1+\frac{1}{\zeta_{w,h}}}\right)\\ \sigma\left({v_{w,h}}\right)=\psi_{w,h}^{2}\cdot\vartheta\left({1+\frac{2}{% \zeta_{w,h}}}\right)-\left[{\mu\left({v_{w,h}}\right)}\right]^{2}\\ \end{array}}\right.$ (3)

In Eq. (3), $\gamma$ is the mathematical expectation operator of the gamma function, and the forecast is more accurate the smaller it is, according to $\sigma\left({v_{w,h}}\right)$ , $\vartheta$ stands for gamma function. Involvement in renewable energy and clean energy in VPP is very important, and photovoltaic power generation has been widely used because of new energy without regional restrictions [17]. At present, the main type of generator used in the photovoltaic power generation model is a grid-connected photovoltaic power generation system. And the estimation of photovoltaic energy production $P_{\textit{ppg}}$ is displayed in Eq. (4).

$\displaystyle P_{\textit{ppg}}=S\cdot\textit{LI}\cdot\chi_{\textit{mpt}}\chi_{% \textit{inv}}\left({1-0.005\left({\textit{AT}+25}\right)}\right)$ (4)

In Eq. (4), $S$ is the effective area of the photovoltaic panel receiving light. LI is the light intensity at this moment. $\chi_{\textit{mpt}}$ and $\chi_{\textit{inv}}$ are the tracking efficiency and inverter efficiency of the maximum power point in the photovoltaic power generation system. AT represents the ambient temperature. Gas turbine is a commonly used energy coupling device in VPP, which is used to connect two different energy systems [18]. The calculation of power conversion and fuel consumption between gas turbine and electricity, natural gas network and other virtual power plant, as well as the gas turbine model’s expressiveness, are displayed in Eq. (5).

$\displaystyle\left\{{\begin{array}[]{l}P_{\textit{gt,r}}\left(t\right)=\frac{P% _{\textit{gt}}\left(t\right)\chi_{t}}{\chi_{e}}\\ V_{\textit{gt}}=\sum\limits_{t=1}^{i}{\left[{\frac{P_{\textit{gt}}\left(t% \right)}{\chi{}_{e}\textit{LHV}_{\textit{ges}}}\Delta t}\right]}\\ \end{array}}\right.$ (5)

In Eq. (5), $P_{\textit{gt,r}}\left(t\right)$ , $P_{\textit{gt}}\left(t\right)$ are respectively the power generation power and presently produced thermal power by the gas turbine, $\chi_{t}$ and $\chi_{e}$ jointly with $t$ are the thermal and electrical efficiency coefficients. $V_{\textit{gt}}$ represents the total gas consumption of natural gas in one scheduling cycle of the gas turbine, which $\textit{LHV}_{\textit{ges}}$ is natural gas’s low calorific value, according to previous studies, the value is 9.7 kWh/m ${}^{3}$ [19]. The gas boiler is a coupling device similar to the gas turbine, through which the natural gas and heat pipeline system completes energy conversion. Its structure is shown in Fig. 2.

Figure 2.

Gas fired boiler structure.

The principle of the gas boiler is as follows. First, natural gas is input from the gas port, fully burned in the combustion chamber, and water is input from the water inlet to the boiler. The burned natural gas heats the water, then it is sent into the pipeline through the water pump into the room for heat dissipation. After the heat dissipation is completed, the temperature of the water body drops. Finally the above steps are repeated. The relationship between the output thermal power and the volume of natural gas consumed in the gas boiler model is shown in Eq. (6).

$\displaystyle\left\{{\begin{array}[]{l}P_{\textit{gfbt}}\left(t\right)=P_{% \textit{gfb}}\left(t\right)+P_{1}\left(t\right)+P_{2}\left(t\right)\\ V_{\textit{gfb}}=\sum\limits_{t=1}^{i}{\frac{P_{\textit{gfbt}}\left(t\right)}{% \textit{LHV}_{\textit{ges}}}}\\ \end{array}}\right.$ (6)

In Eq. (6), $P_{\textit{gfbt}}\left(t\right)$ , $P_{\textit{gfb}}\left(t\right)$ , $P_{1}\left(t\right)$ , and $P_{2}\left(t\right)$ are respectively the total power of the gas-fired boiler, the output thermal power, the heat loss caused by incomplete combustion and the escape heat loss at the moment $t$ . $V_{\textit{gfb}}$ indicates the entire amount of natural gas that the gas-fired boiler uses throughout the course of a 24-hour cycle. The waste heat boiler uses the preheating of gas turbine and gas boiler waste gas as a heat source for repeated use. The calculation of the waste heat boiler model is shown in Eq. (7).

$\displaystyle P_{\textit{whb}}\left(t\right)=\chi_{\textit{whb}}P_{in}\left(t\right)$ (7)

In Eq. (7), $P_{\textit{in}}\left(t\right)$ represents the input thermal power, $P_{\textit{whb}}\left(t\right)$ represents the output thermal power of the waste heat boiler $\chi_{\textit{whb}}$ at any time $t$ , which is the waste heat boiler’s heat recovery efficiency coefficient jointly.

3.2 Design of generalized energy storage model of virtual power plant

VPP energy storage equipment model includes a battery model and a heat storage tank model. The battery model only studies the fixed energy storage composed of batteries, and uses a circuit model that can effectively reflect the electrical characteristics of the battery port. The battery model expression is Eq. (8).

$\displaystyle\textit{SOC}_{b}\left(t\right)=\textit{SOC}_{b}\left({t-1}\right)% +\frac{\chi_{b}^{c}P_{b}^{c}\left(t\right)-{P_{b}^{d}\left(t\right)}\mathord{% \left/{\vphantom{{P_{b}^{d}\left(t\right)}{\chi_{b}^{d}}}}\right.\kern-1.2pt}{% \chi_{b}^{d}}}{C_{b}}$ (8)

In Eq. (8), $\textit{SOC}_{b}\left(t\right)$ represents the state of charge. $\chi_{b}^{c}$ and $\chi_{b}^{d}$ are the storage battery’s charging and discharging efficiency coefficients, respectively. $P_{b}^{c}\left(t\right)$ and $P_{b}^{d}\left(t\right)$ indicate the storage battery’s current capacity for charging and discharging. $C_{b}$ represents the storage battery’s remaining battery capacity. The heat storage tank’s mathematical model is comparable to the battery’s. When heat storage tank performs heat storage operation at any time $t$ , or when it chooses to release heat to supply regional users or sell heat energy, the stored heat at time $t$ is $\textit{SOC}_{hst1}\left(t\right)$ and $\textit{SOC}_{hst2}\left(t\right)$ respectively, see Eq. (9).

$\displaystyle\left\{{\begin{array}[]{l}\textit{SOC}_{\textit{hst1}}\left(t% \right)=\textit{SOC}_{\textit{hst1}}\left({t-1}\right)+\chi_{\textit{hst}}^{c}% P_{\textit{hst}}^{c}\left(t\right)\\ \textit{SOC}_{\textit{hst2}}\left(t\right)=\textit{SOC}_{\textit{hst2}}\left({% t-1}\right)-P_{\textit{hst}}^{d}{\left(t\right)}\mathord{\left/{\vphantom{{% \left(t\right)}{\chi_{\textit{{hst}}}^{d}}}}\right.\kern-1.2pt}{\chi_{hst}^{d}% }\\ \end{array}}\right.$ (9)

In Eq. (9), $P_{\textit{hst}}^{c}\left(t\right)$ and $P_{\textit{hst}}^{d}\left(t\right)$ are the heat storage tank’s charging and discharging power at all times, respectively. $\chi_{\textit{hst}}^{c}$ and $\chi_{\textit{hst}}^{d}$ are the battery’s thermal efficiency during charging and discharge. In VPP, users can randomly select the type of energy used to meet their own energy demand. This load is an alternative load [20]. The study adopts the load-side electric heat replacement and electric gas replacement load. And it realizes the corresponding guidance on load replacement through the cost price of electric heat gas in VPP. The expression of the multi-energy alternative load model is Eq. (10).

$\displaystyle\left\{{\begin{array}[]{l}\Delta L_{j}=-\delta_{jk}\Delta L_{k}\\ \delta_{jk}={\textit{CV}_{j}\chi_{j}}\mathord{\left/{\vphantom{{\textit{CV}_{j% }\chi_{j}}{\textit{CV}_{k}\chi_{k}}}}\right.\kern-1.2pt}{\textit{CV}_{k}\chi_{% k}}\\ \end{array}}\right.$ (10)

In Eq. (10), $j$ and $k$ are the two energy sources of electricity, heat and natural gas, respectively. $\Delta L_{j}$ and $\Delta L_{k}$ represent the load increment of the two energy sources. $\delta_{jk}$ represents the change ratio of the two energy sources before and after load substitution. $\textit{CV}_{j}$ and $\textit{CV}_{k}$ are $j$ the unit calorific values $j$ of energy and natural gas $\chi_{j}$ , respectively. $k$ and $\chi_{k}$ represents the utilization $j$ of $k$ . In the VPP, electric vehicles are mostly used by residents, so the research adopts the charging and discharging optimization scheduling strategy for the electric vehicle model. The technology of integrating electric vehicles into the power grid can allow them to switch between the two attributes of source and load. The time-space translation of electric energy is realized, and electric vehicles to participate in the operating costs of the VPP system are allowed [21]. At the time $t$ of selective charging and discharging, the electric vehicle’s status of charge is $\textit{SOC}_{ev1}\left(t\right)$ and $\textit{SOC}_{ev2}\left(t\right)$ , respectively. And the calculation is shown in Eq. (11).

$\displaystyle\left\{{\begin{array}[]{l}\textit{SOC}_{\textit{ev1}}\left(t% \right)=\textit{SOC}_{\textit{ev1}}\left({t-1}\right)+\chi_{\textit{ev}}^{c}P_% {\textit{ev}}^{c}\left(t\right)\\ \textit{SOC}_{\textit{ev2}}\left(t\right)=\textit{SOC}_{\textit{ev2}}\left({t-% 1}\right)-P_{\textit{ev}}^{d}{\left(t\right)}\mathord{\left/{\vphantom{{\left(% t\right)}{\chi_{\textit{ev}}^{d}}}}\right.\kern-1.2pt}{\chi_{\textit{ev}}^{d}}% \\ \end{array}}\right.$ (11)

In Eq. (11), $\chi_{\textit{ev}}^{c}$ and $\chi_{\textit{ev}}^{d}$ represent the storage battery’s charging and discharging efficiency coefficient, jointly with $P_{\textit{ev}}^{c}\left(t\right)$ . $P_{\textit{ev}}^{d}\left(t\right)$ represents the heat storage tank’s capacity for both charging and discharging at moment $t$ . When investigating the air conditioning model, because the heat transfer of the actual building is very complicated, it is assumed that the material in the whole building is uniformly distributed. And the equivalent heat-melt index in the equivalent mathematical model of the air-conditioning can be used to describe the heat storage characteristics of the building. According to the positive correlation between building heat dissipation $Q_{s}$ and temperature difference inside and outside the building, and the law of energy conservation, the building heat equation can be obtained, and the calculation is shown in Eq. (12).

$\displaystyle\left\{{\begin{array}[]{l}Q_{c}=C\frac{dT_{\textit{in}}}{\textit{% dt}}\\ Q_{s}=\frac{T_{\textit{in}}-T_{\textit{out}}}{R}\\ Q_{\textit{ac}}+Q_{s}=Q_{c}+Q_{d}\\ \end{array}}\right.$ (12)

In Eq. (12), $Q_{c}$ is the heat storage power of the building $C$ , which is the building’s equivalent heat capacity. $T_{\textit{in}}$ and $T_{\textit{out}}$ are temperatures inside and outside, respectively. $R$ is the equivalent thermal resistance of the building. $Q_{\textit{ac}}$ is the cooling power of the air conditioner. $Q_{d}$ is the amount of interference heat generated by personnel activities, external radiation and equipment work. In actual engineering, $Q_{d}$ numerical value is little. In order to simplify the model, the commonly used first-order ETP model of air conditioning - building system is adopted in the study, and its structure is shown in Fig. 3.

Figure 3.

Structure of the first order ETP model of air conditioning building system.

In Fig. 3, the ETP model regards the building heat capacity as the equivalent heat capacity, sets the equivalent thermal resistance as the resistance. And the indoor and outdoor node temperature are regarded as the voltage source of different two points in the circuit. The differential equation of the first order ETP model can be obtained by converting the first order ETP model of the air-conditioning building system into an equivalent circuit and solving it with Kirchhoff theorem. Once the air conditioner’s interior temperature is set and is at the recommended comfortable level, the electric power of the air conditioner $P_{\textit{ac0}}$ can be obtained as Eq. (13).

$\displaystyle\left\{{\begin{array}[]{l}\frac{dT_{\textit{in}}\left(t\right)}{% \textit{dt}}=0\\ P_{\textit{ac0}}=\frac{T_{\textit{out}}\left(t\right)-T_{\textit{in}}\left(t% \right)}{\chi{}_{\textit{ac}}\cdot R}\\ \end{array}}\right.$ (13)

In Eq. (13), $\chi_{\textit{ac}}$ is the thermoelectric conversion coefficient of the air conditioner. According to previous research results, when the economic incentives change within a certain range, users will change their electricity consumption habits. During the response process, residents’ air conditioners will receive economic subsidies, but comfort will lose [22]. According to the survey, users are more sensitive to temperature rise, so the adjustment cost is higher than that in the temperature range, and the compensation cost in the non-sensitive range is the same, as shown in Eq. (14).

$\displaystyle\textit{ACC}_{ac}=\left\{{\begin{array}[]{l}c_{1},T_{\textit{down% }}\leqslant T_{\textit{in}}\leqslant T_{\textit{up}},T_{\textit{in}}\neq T_{% \textit{conf}}\\ c_{2},T_{\min}\leqslant T_{\textit{in}}\leqslant T_{\textit{down}}\\ c_{3},T_{\textit{up}}\leqslant T_{\textit{in}}\leqslant T_{\max}\\ \end{array}}\right.$ (14)

In Eq. (14), $\textit{ACC}_{ac}$ is the compensation cost of air conditioning adjustment. $c_{1}$ , $c_{2}$ and $c_{3}$ are the corresponding compensation costs under different adjustment degrees.

3.3 Building a virtual power plant operation optimization model based on universal energy storage

When examining VPP’s operational optimization technique, it is necessary to effectively link the electricity-heat-gas-carbon multi-market with different generalized energy storage equipment. And an operation optimization strategy based on the generalized energy storage VPP model under multi-market coordination is proposed. The research adds power-to-gas coupling equipment to the VPP model, so that the power-heat-gas-carbon multi-coordination market can be included in the participation in the power day-ahead dispatch and intraday balance markets. To make the computation of the chemical reaction efficiency of hydrogen generation by electrolysis of water and hydrogen methanation in the power-to-gas conversion simpler, the model of electrolyzer and methane reactor is established as shown in Eq. (15).

$\displaystyle\left\{{\begin{array}[]{l}P_{\textit{eco}}\left(t\right)=\chi_{% \textit{ec}}P_{\textit{eci}}\left(t\right)\\ P_{\textit{ec}}^{\min}\leqslant P_{\textit{eci}}\leqslant P_{\textit{ec}}^{% \max}\\ \Delta P_{\textit{ec}}^{\min}\leqslant P_{\textit{eci}}\left({t+1}\right)-P_{% \textit{eci}}\left(t\right)\leqslant\Delta P_{\textit{ec}}^{\max}\\ \end{array}}\right.$ (15)

In Eq. (15), $P_{\textit{eco}}\left(t\right)$ and $P_{\textit{eci}}\left(t\right)$ stand for the current output and input power of the electrolyzer, respectively. $\chi_{\textit{ec}}$ represents the conversion efficiency of the electrolyzer. $P_{\textit{ec}}^{\min}$ and $P_{\textit{ec}}^{\max}$ are the electrolyzer’s maximum power and minimum power. $\Delta P_{\textit{ec}}^{\min}$ and $\Delta P_{\textit{ec}}^{\max}$ are the climbing power limit of the input power of the electrolyzer. Based on the limitation of VPP capacity, the study assumes that the virtual power plant participates in several marketplaces as a price taker. And it only needs to optimize the market’s virtual power plant’s scheduling method according to the anticipated production of wind energy and market taken any action. The VPP framework based on generalized energy storage can be obtained as shown in the Fig. 4.

Figure 4.

VPP framework based on generalized energy storage.

Figure 4 shows that the research expands the electricity market into the energy market of the electric heating market. At each moment, VPP sells the excess electricity and thermal power produced to the market for profit. The marketers can be transformed into producers and sellers who can actively participate in the market to realize the synergistic coupling between the markets for electricity and natural gas. In addition, research will benefit from selling the heat generated in the reaction of power-to-gas equipment through the heat pipe network. Increase the revenue of virtual power plant in the carbon trading market and provide power-to-gas equipment more access to VPP Later gravitational capacity, according to the carbon trading market’s trading process. Finally, through multi-energy coupling equipment such as gas turbines, the coordinated energy, natural gas, and carbon emission markets enable the optimization of VPP’s operational processes. To address the energy supply changes brought on by the unpredictability of wind and solar power output, the research used the C&CG column constraint approach. The objective function is the strategy utilizing the multi-coordinated market’s virtual power plant’s lowest operational cost, jointly with its calculation is shown in Eq. (3.3).

$\displaystyle\mathop{\min}\limits_{\beta_{1}}\mathop{\max}\limits_{\beta_{2}}% \sum\limits_{t=1}^{T}\left(f_{\textit{pun}}\left(t\right)+f_{\textit{ev}}\left% (t\right)+f_{\textit{ac}}\left(t\right)+f_{\textit{eh}}\left(t\right)+f_{% \textit{env}}\left(t\right)+f_{c}\left(t\right)+f_{\textit{gas}}\left(t\right)% +\right.$ (16) $\displaystyle\quad\left.f_{\textit{p2g}}\left(t\right)+f_{\textit{res}}\left(t% \right)\right)$

In Eq. (3.3), $\beta_{1}$ is the decision variable of each equipment output of VPP’s output in real time $t$ . $\beta_{2}$ is the real amount of power produced by solar and wind energy, together with the objective purpose includes the time $t$ balance market deviation penalty cost $f_{\textit{pun}}\left(t\right)$ , electric vehicle compensation cost $f_{\textit{ev}}\left(t\right)$ , alternative load response cost $f_{\textit{res}}\left(t\right)$ , and air conditioning virtual energy storage adjustment cost $f_{\textit{ac}}\left(t\right)$ , gas turbine environmental cost $f_{\textit{env}}\left(t\right)$ . $f_{\textit{gas}}\left(t\right)$ , $f_{\textit{eh}}\left(t\right)$ and $f_{c}\left(t\right)$ are the current market revenues for natural gas, electric heating energy, and carbon trading of VPP. $f_{\textit{p2g}}\left(t\right)$ is the operating costs of power-to-gas equipment. The supply and demand balance of various energy sources in the VPP, such as electricity, thermal energy, natural gas, and carbon, constitute the first restriction. And the calculation is shown in Eq. (17).

$\displaystyle\left\{{\begin{array}[]{l}\left({\begin{array}[]{l}P_{\textit{gt}% }\left(t\right)+P_{\textit{winp}}\left(t\right)+P_{\textit{ppg}}\left(t\right)% +P_{b}^{d}\left(t\right)+\sum\limits_{j=1}^{K}{P_{ev,j}^{d}\left(t\right)}+P_{% \textit{grid}}^{\textit{bu}}\left(t\right)\\ =P_{\textit{grid}}^{\textit{se}}\left(t\right)+\sum\limits_{j=1}^{K}{P_{% \textit{ev,j}}^{c}\left(t\right)}+P_{\textit{p2g}}\left(t\right)+P_{b}^{c}% \left(t\right)+P_{\textit{el}}\left(t\right)+\Delta P_{\textit{el}}\left(t% \right)\\ \end{array}}\right)\\ P_{\textit{whb,r}}\left(t\right)+P_{\textit{gfb}}\left(t\right)+P_{\textit{hst% }}^{d}\left(t\right)=P_{\textit{hst}}^{c}\left(t\right)+P_{hl}\left(t\right)+% \Delta P_{\textit{hl}}\left(t\right)\\ P_{\textit{gas}}^{\textit{se}}\left(t\right)=P_{\textit{p2g,r}}\left(t\right)-% {P_{\textit{gt}}\left(t\right)}\mathord{\left/{\vphantom{{P_{\textit{gt}}\left% (t\right)}{\chi_{\textit{gt}}-{P_{\textit{gfb}}\left(t\right)}\mathord{\left/{% \vphantom{{P_{\textit{gfb}}\left(t\right)}{\chi_{\textit{gfb}}+P_{\textit{gt}}% \left(t\right)-\Delta P_{\textit{gt}}\left(t\right)}}}\right.\kern-1.2pt}{\chi% _{\textit{gfb}}+P_{\textit{gt}}\left(t\right)-\Delta P_{\textit{gt}}\left(t% \right)}}}}\right.\kern-1.2pt}{\chi_{\textit{gt}}-{P_{\textit{gfb}}\left(t% \right)}\mathord{\left/{\vphantom{{P_{\textit{gfb}}\left(t\right)}{\chi_{% \textit{gfb}}+P_{\textit{gt}}\left(t\right)-\Delta P_{\textit{gt}}\left(t% \right)}}}\right.\kern-1.2pt}{\chi_{\textit{gfb}}+P_{\textit{gt}}\left(t\right% )-\Delta P_{\textit{gt}}\left(t\right)}}\\ M_{\textit{gt}}\left(t\right)+M_{\textit{gfb}}\left(t\right)+M_{\textit{p2g}}% \left(t\right)=2M_{\textit{gtp}}\left(t\right)+M_{\textit{ex}}\left(t\right)\\ \end{array}}\right.$ (17)

In Eq. (17), $P_{\textit{p2g}}\left(t\right)$ is the electric power supplied by VPP to the operation of the electric converter equipment at all times. $P_{\textit{whb,r}}\left(t\right)$ is the released heat power collected by methanation when the power-to-gas equipment is running at all times. $M_{\textit{ex}}\left(t\right)$ is the carbon emission quota traded by VPP $t$ with other participants in the carbon trading market at all times. $M_{\textit{gt}}\left(t\right)$ and $M_{\textit{gtp}}\left(t\right)$ are the free quotas and actual carbon emissions obtained by the gas turbines and boilers in the VPP, respectively. $M_{\textit{gfb}}\left(t\right)$ and $M_{\textit{p2g}}\left(t\right)$ are the CO ${}_{2}$ absorbed by the power-to-gas equipment during operation. Then there is the interaction constraint between VPP and power grid, see Eq. (18).

$\displaystyle\left\{{\begin{array}[]{l}0\leqslant P_{\textit{grid}}^{\textit{% bu}}\left(t\right)\leqslant P_{\textit{grid}}^{\textit{bu},\max}\left(t\right)% \\ 0\leqslant P_{\textit{grid}}^{\textit{se}}\left(t\right)\leqslant P_{\textit{% grid}}^{\textit{se},\max}\left(t\right)\\ \end{array}}\right.$ (18)

In Eq. (18), $P_{\textit{grid}}^{\textit{bu},\max}\left(t\right)$ is the upper limit of interaction between VPP and power grid at each moment. Then $P_{\textit{grid}}^{\textit{se},\max}\left(t\right)$ is the constraint of power-to-gas equipment, see Eq. (19).

$\displaystyle\left\{{\begin{array}[]{l}P_{\textit{p2g}}^{\min}\leqslant P_{% \textit{p2g}}\leqslant P_{\textit{p2g}}^{\max}\\ Z_{\textit{p2g}}^{-}\leqslant P_{\textit{p2g}}\left(t\right)-P_{\textit{p2g}}% \left({t-1}\right)\leqslant\\ P_{\textit{p2g,g}}\left(t\right)=\chi_{\textit{p2g}}P_{\textit{p2g}}\left(t% \right)\\ \end{array}}\right.Z_{\textit{p2g}}^{+}$ (19)

In Eq. (19), $P_{\textit{p2g}}^{\max}$ and $P_{\textit{p2g}}^{\min}$ are the operational power of the power-to-gas equipment’s top and lower limits. $Z_{\textit{p2g}}^{-}$ and $Z_{\textit{p2g}}^{+}$ are the smallest and largest ramp rates of the power-to-gas equipment. $\chi_{\textit{p2g}}$ indicates the power-to-gas equipment’s energy conversion efficiency coefficient. Gas turbine, gas boiler and preheating boiler in VPP are consistent with the above Eq. (19). Using the column constraint generation approach, the model’s solution divides the initial optimization issue into a primary problem and two sub problems, and solve iteratively in two stages. In this process, intermediate variables need to be introduced, the main problem can be obtained through dismantling. And the optimal solution can be found to find the worst scenario caused by the uncertainty of renewable energy processing, that is, sub-problem 1. In the worst scenario, simulate the air conditioner’s virtual energy storage to take part in real-time VPP adjustment. And the charge and discharge of the air conditioner’s virtual energy storage is optimized, which is sub-problem 2. Finally, it is judged whether the model is converged, and the optimal solution based on the generalized energy storage VPP operation optimization model can be obtained. To sum up, the flow of the algorithm based on column constraint generation can be obtained, as shown in Fig. 4.

Figure 5.

Process based on column constraint generation algorithm.

In Fig. 5, the initialization operation is required first, and then the auxiliary variables are introduced. In the first iteration, the virtual energy storage output of the initialization wind and air conditioning is used for deterministic optimization. In the subsequent iteration, all the worst risk scenarios found in the $k-1$ iteration and the virtual energy storage output of the air conditioner are used to optimize the operation and calculate the final operation cost. The third step is to find the bad scenario caused by the uncertainty of renewable energy output according to the optimal solution of various problems, namely the optimization result of sub-problem 1. The fourth step is to find the total cost of the minimum penalty cost based on the solution of sub-problem 1. Finally, whether the algorithm converges is judged, that is, the main problem and the sub-problem iterate each other to meet the convergence conditions.

4. Scheduling optimization analysis based on generalized energy storage virtual power plant operation optimization model

4.1 Analysis of VPP scheduling optimization results in the electricity-heat-gas-carbon multi-market

The scheduling optimization results are analyzed in order to confirm the accuracy of the generalized energy storage virtual power plant operation optimization model put forth in the study. Due to the capacity limitation of VPP itself, the study assumes that VPP is a price taker in each market. And it only needs to optimize the scheduling strategy of VPP in the market according to the anticipated output of wind energy and market transaction price. According to the predicted values of wind power and photovoltaic power and the predicted values of electricity purchase and sale prices and multi-energy loads, the VPP system parameters can be obtained as follows. The heat trading market price is 36.75 $/MWh, and the stationary energy consumption of the power-to-gas equipment is 0.18 MW. The price of carbon on the market for trading is 21.62 $/t, and the operating cost coefficient is 17.00$/MWh. The gas turbine’s carbon emissions the resistance is 0.065 t/GJ, and its quota coefficient is 0.102 t/GJ. The carbon emission quota for gas-fired boilers is 0.0623 t/GT, and the carbon emission intensity for burning natural gas is 2.01 kg/10 ${}^{6}$ m ${}^{3}$ .

Figure 6.

Dispatching results of virtual market in electric heating market.

Figure 6 shows the results of VPP scheduling in the electric heating market. Figure 6-(a) shows that when transaction price fluctuations in various markets are compared, all equipment works in accordance with the principles of economy and low-carbon environmental protection. Electric vehicles, energy storage and alternative loads Charging or converting other types of loads into electric loads when electricity prices are low. It needs discharge when the electricity price is high is consistent with converting the electrical load to other sources. Thereby the operating cost of the VPP and saving energy is reduced. The power of the power-to-gas equipment shows that the power-to-gas operation is performed during the 0–7 and 23–24 periods. At this time, VPP receives the least advantage from selling power to the grid, jointly with the gas turbine processing is maintained at the lowest level, which improves the operating economy of the system. Figure 6-(b) shows that it has not yet joined the heat market to sell excess heat energy, and the overall heating cost is low due to the large residual heat power of gas turbine. Therefore, the electricity-heat alternative load shows that the thermal load replaces the electric energy at most times to reduce the cost of electricity. The power of gas-fired boiler fluctuates with the change of natural gas and electricity market prices. On the basis of meeting the heat load, the excess heat is stored by the heat storage tank when the heating cost is low. When the heating cost is high, the heat storage tank releases heat to reduce the boiler heating to reduce the gas consumption cost. In addition, it can effectively reduce the carbon emissions caused by the combustion of natural gas in the boiler.

Figure 7.

Results of virtual power plant in the market for gas and carbon trading.

Figure 7 shows the VPP scheduling results in the market for trading gas and carbon. Figure 7-(a) shows that alternative gas loads can comprehensively compare the cost of gas and electricity, as well as increasing VPP revenue in different markets in different periods. During the period of low electricity price of electricity to gas equipment, it is necessary to convert the surplus electricity into natural gas to reduce the overall gas purchase cost of the system. By recovering its reaction waste heat, part of the working power of the gas-fired boiler is reduced and the gas consumption of the boiler is reduced. And the carbon emissions generated by natural gas combustion in the boiler is effectively reduced. For the increment of alternative gas load, considering the marginal effect, the electricity price and gas price are comprehensively compared, and the electricity load is converted into gas load at the time of high price. At the time of low electricity price, the gas load is converted to the electricity load to realize the space-time transfer between multi-energy loads. The income of VPP is increased in different markets at different time periods and the operation and dispatching costs is reduced. Figure 7-(b) shows that VPP benefits from the trading market by selling excess carbon allowances. Participating in the market for carbon trading, gas turbines and gas boilers will adjust their output through their own carbon trading quotas and actual carbon emission intensity. During the power-to-gas working period, the CO ${}_{2}$ absorption reaction function increases the carbon quota trading volume during this period, which greatly promotes the environmental protection and economy of VPP.

4.2 Impact of different participating markets on VPP operation

Table 1
Four scenarios for VPP construction

Scene	1	2	3	9
Energy market	Y	Y	Y	Y
Electric to gas equipment	N	N	Y	Y
Natural gas market	Y	Y	Y	Y
Carbon trading market	N	Y	N	Y
Generalized energy storage	Y	Y	Y	Y
Gas turbine	Y	Y	Y	Y
Gas fired boiler	Y	Y	Y	Y

Four distinct VPP operation scenarios have been researched and developed in order to confirm that the research aggregated VPPs have superior economies. And it can deliver more advantages by engaging in the multi collaborative market. For particular cases, see Table 1. The data of the study is from the VPP of the State Power Investment Corporation. Scenario 4 in Table 1 includes all contents of power-to-gas equipment, gas turbines, gas-fired boilers, generalized energy storage, energy market, natural gas market and carbon trading market. Scenario 2 eliminates the content of the power-to-gas equipment, scenario 3 removes the content of the carbon trading market. Scenario 1 removes the contents of power-to-gas equipment and carbon trading market. And scenario 1 is the VPP operation scenario of document [8], scenario 2 is the aggregated VPP operation scenario proposed by the study, scenario 3 is the VPP operation scenario of document [11], and scenario 4 is the VPP operation scenario of document [13].The research takes 24 h as the total electricity duration and 1 h as the step size to conduct experiments, and uses MATLAB for modeling and solution.

Table 2

The corresponding costs in the four scenarios

Scene	1	2	3	4
Energy market income ($)	5847.91	5947.99	5715.20	5731.81
Natural gas market income ($)	$-$ 7700.53	$-$ 7803.65	$-$ 7436.60	$-$ 7403.64
Carbon trading market income ($)	0	144.93	0	144.93
Compensation cost of electric vehicle ($)	48.18	48.18	48.18	48.18
Response cost of alternative load ($)	49.34	48.39	49.34	48.39
Air conditioning cluster regulation cost ($)	28.87	28.87	30.48	26.78
Cost of electricity to gas equipment ($)	0	0	94.34	142.98
Deviation penalty cost ($)	355.74	355.74	372.55	378.86
Total operation cost ($)	2334.76	2191.91	2316.31	2135.52

MATLAB may be used to determine the optimization outcomes of VPP operation in the multi-collaborative market under four situations, as indicated in Table 2. Table 2 shows that in Scenario 1, the only way to achieve multi-energy time transfer and lower VPP operating costs is by managing electric vehicles and alternative loads in the power market. The advantages of the energy market will rise by $ 116.10 in comparison to Scenario 4. But the benefits of the natural gas market will decline by $296.88. And the overall operating expenses will rise by $ 199.24. In contrast to Scenario 4, Scenario 2 demonstrates that the revenue from the energy market rises by $ 216.18, the revenue from the natural gas market falls by $400.00, the revenue from the carbon trading market falls by $ 36.58, and the final operating cost rises by $ 56.39. The income from the energy market falls by $ 132.71 in Scenario 3 compared to Scenario 1, while the revenue from the natural gas market rises by $ 263.92. However, the total operating cost increased by $ 124.41 when compared to the three markets in Scenario II. The company obtained $ 181.51 in the carbon trading market through the polymerization of electricity to gas equipment. And the final operating cost decreased by $ 180.80, or 7.81%, compared to the three phases of scenario IV. The income of the energy market is the same. The income of the natural gas market increases from $-$ 7436.60 to $-$ 7403.64, a rise of $ 32.96. The findings demonstrate that VPP polymerized electricity to gas equipment in Scenario 4 is more flexible and ecologically beneficial while engaging in the electricity, heat, gas, and carbon trading markets at the same time. It also has the lowest operating costs.

Figure 8.

Gas boiler, gas turbine, and carbon trading market allotment output power under various situations.

To confirm the impact of participation in the carbon trading market on the operation of VPP, the output power of gas turbines and gas boilers as well as the sales quotas in the carbon trading market were acquired, as shown in Fig. 8. Scenarios 1 and 2 were chosen for comparison. Figure 8-(a) shows that in Scenario 2, once VPP joins the carbon trading market, the income from carbon trading and the energy market is lower than the cost of gas turbine power generation, and the output of gas turbines stays steady. When power prices were very high, comparing the two scenarios of the gas turbine, the second scenario can obtain more benefits. Figure 8-(b) shows that the power change of the output equipment is consistent with the advantage of the second scenario in the carbon trading market, which is greater than that of the first scenario. The carbon trading quota is enhanced in the 14–20 timeframe.

Figure 9.

Selling power under different scenarios.

Figure 10.

Changes in VPP carbon emissions and carbon trading under different scenarios.

The power sales outcomes of scenarios 2 and 4 are compared to produce the findings presented in Fig. 9. It serves as confirmation of the impact of integrated p2g devices on the performance of VPP in a multi-cooperative market. Figure 8 demonstrates that following the combination of equipment for power-to-gas, in the period of 1–7 and 23–24, VPP supplies the surplus power to the power-to-gas equipment in the day-ahead dispatch market. And it can obtain benefits in heat-gas-carbon, which is higher than that of the previous Revenue from grid electricity sales. As a result, at this time, Scenario 4’s electricity sold to the grid is less than Scenario 2’s. As a result, the energy market’s income declines.

Figure 10 shows the results of VPP carbon emissions and carbon trading changes in Scenario 2 and Scenario 9. Figure 10 shows that in the carbon trading market, power-to-gas equipment can effectively reduce the actual emissions of VPP through methanation and achieve a more low-carbon energy supply. And the carbon rights trading volume of VPP in the carbon trading market was increased, the income was increased by 25.24%, and the operating cost of VPP was reduced.

Figure 11.

Efficiency of p2g operation and daily operating expense of VPP under various carbon trading prices.

The carbon trading price will fluctuate to support the policies of carbon neutrality and carbon peaking, which will have an impact on each VPP piece of equipment’s processing state. Figure 11 shows the changes in p2g operating efficiency and daily operating cost under different carbon trading prices. Figure 11-(a) demonstrates how the power-to-gas equipment will profit more from the carbon trading market as the price of carbon trading rises and how the working power will rise. The power-to-gas equipment’s daily operating pattern is unaffected when the carbon trading price is more than 64.85, and the quality of carbon absorbed remains stable. Figure 11-(b) shows that the trading quota for VPP in the carbon trading market will rise in line with the rise in carbon trading prices, and the power-to-gas machinery will also be upgraded. Therefore, the overall operating cost of VPP increases with the carbon trading price. Growth shows a downward trend. In the period of 1–7 and 23–24, VPP supplies the surplus power to the power-to-gas equipment in the day-ahead dispatching market. And it can obtain income from heat-gas-carbon, which is higher than the income from electricity sales to the grid.

5. Conclusion

In recent years, the policy of carbon neutrality and carbon peaking has been continuously promoted, and it is very important for industries to realize low-carbon strategies. VPP can effectively promote the synergy of energy sources on both sides of the source and load, and improve the economy and flexibility of energy dispatching. But it is very prone to problems such as unstable energy supply caused by random processing fluctuations of renewable energy. To address the issues mentioned above, research introduces virtual energy storage such as electric vehicles and alternative response loads. At the same time, it cooperates with gas turbines and gas boilers to form VPP, and constructs an operation optimization model based on generalized energy storage NPP under the electric-heat-gas-carbon multi-synergy market. The findings of the experiment demonstrate that when the equipment’s power to produce gas is running in the 0–7 and 23–24 periods. The benefit of VPP’s electricity sales to the grid is the smallest, and the gas turbine processing is maintained at the lowest level. When the market returns for Scenario 4 and Scenario 3 were compared, the returns on the energy market were unchanged, while the returns on the natural gas market increased by USD 32.96, from -USD 7436.60 to -USD 7403.64. Additionally, the company earned USD 181.51 from the carbon trading market by using equipment that converts electricity to gas, and its overall operating costs dropped by USD 180.80, or 7.81%. The power-to-gas technology boosts VPP’s carbon trading volume in the carbon trading market, and the income increases by 25.24% as a result. To sum up, the suggested generalized energy storage-based VPP operation optimization model can strengthen the connection between energy and the electricity-heat-gas-carbon market. The economics of VPP operation is improved effectively, thus achieving the objective of low-carbon environmental preservation. However, the research still needs to be done. The research is mainly based on the optimal overall scheduling, ignoring the distribution of benefits among different stakeholders. In the future, the problem of distribution of benefits among stakeholders under optimal scheduling can be added.

Footnotes

Funding

The work was financially supported by Science and Technology Projects from State Grid Corporation of China, (Research and Application of Key Technologies for Economic Operation of Virtual Power Plant Considering Real-time Carbon Flow, 5108-202218280A-2-383-XG).

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The low-carbon economic operation strategy of virtual power plant under different electricity-gas-heat-carbon multi-market synergy scenarios

Abstract

Keywords

1. Introduction

2. Related work

3. Building a virtual power plant operation optimization model based on universal energy storage

3.1 Construction of the virtual power plant’s source layer equipment model

4.1 Analysis of VPP scheduling optimization results in the electricity-heat-gas-carbon multi-market

Table 1 Four scenarios for VPP construction

Footnotes

Funding

References

Table 1
Four scenarios for VPP construction