Electricity Market Environment Research Articles

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

Optimal bidding strategy of a wind power producer in Chinese spot market considering green certificate trading.

In China, wind power producers will participate in the spot market as strategic producers. They should submit offering prices and forecasted production to the independent system operator. Intraprovincial and interprovincial green certificate trading, as a mechanism to promote the development of wind power, is advanced in parallel with the spot market. Studying the bidding strategies of wind power producers in the spot market, especially with the introduction of intraprovincial and interprovincial green certificate trading, has great practical significance for the stable operation of wind power producers and the construction of a renewable energy-friendly electricity market. This paper proposes a bi-level stochastic model to simulate the bidding strategy of a wind power producer in the spot market. The upper level problem of the model describes the decision-making process of the wind power producer, and the lower level problems include the clearing process of the spot market and the TGC market, in which the spot market includes the day-ahead market and the balance market, and the TGC market includes the intraprovincial and interprovincial TGC markets. Then, using Karush-Kuhn-Tucker (KKT) conditions, the strong duality theorem, and the big M method, the bi-level model is recast into a mixed integer linear (MIL) model convenient to be solved. Finally, an example is given to analyze the wind power producer's optimal bidding strategy under different electricity market environments and different wind power penetration levels.

A multivariate statistical method for risk parameter scenario generation and renewable energy bidding in electricity markets

To maximize the expected profits and manage the risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate energy bidding strategies, in which the probabilistic scenarios of risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate statistical method for risk parameter scenario generation, which is used by a wind energy system faced with uncertain electricity prices and wind power productions, and it considers the correlation between dependent risk parameters by using historical data directly. The probabilities of scenarios containing correlated risk parameters are calculated by using multivariate histograms, in which the asymmetric correlation between different parameters existing in the historical data are preserved. Additionally, in order to make the stochastic optimization problem with large numbers of scenarios tractable, a multivariate scenario reduction method is used to trim down the scenario number. By solving the stochastic optimization problem, optimal day-ahead bidding curves for the wind energy system are generated, and Douglas–Peucker algorithm is used to fit the bidding curves according to market requirements. Case studies based on real world data in electricity markets are performed to prove the effectiveness of the proposed risk parameter scenario generation method and energy bidding strategies. Finally, conclusions and practical suggestions on future research works are provided.

A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market

As the share of distributed generation increases, so do the opportunities for aggregators to participate in the electricity market. In particular, aggregators participating in both the day-ahead and real-time markets contribute to improving the reliability of the power system. In addition, aggregators seeking additional revenue can benefit from providing reserves in a joint electricity market environment. However, aggregated resources with uncertainty are limited because of the uncertain nature of both reserve provision and the amount of reserves they can provide. Therefore, this study proposes a robust optimization model for an aggregator to formulate a strategy for participation in the day-ahead markets and deploys energy control in the real-time operation. The serving ratio reflects the availability of the aggregator’s reserve participation. Both the deployed up/down power and renewable energy in the real-time operation are considered as uncertain parameters to reflect the uncertainty. In the case study, we analyze the profit-maximization strategy of an aggregator that owns renewable energy resources and energy-storage systems under the variation interval for uncertain parameters and the serving ratio. The bidding strategies vary by the variation interval and the serving ratio.

Strategic risks of reforming the energy market of Ukraine in the context of the energy security

Monopoly structures and complete control over pricing deform the electric energy environment. An example of the European model of reforming the electricity market, which is based on transparency and consumer accessibility to electricity services, is shown. On this basis, the goals, and vectors of reforming the regional markets of electric energy services have been determined. It is foreseen that the reform process should be carried out on a combination of organizational, economic, technical, social, and other goals. The advantages and disadvantages of reforming the electric power market of the region and the country were identified using the author's SWOT analysis method. The proposed closed cycle of reforming the electricity market covers an interchangeable combination of the following components: regional electricity networks, market environment, information environment, financial and credit support, integration, labor resources, and communication links. Vectors of further reform are outlined. Competitiveness presupposes the free choice of a supplier by consumers in the absence of barriers to the supply and sales market. The new European format of the electricity market model creates new challenges and threats to the operation of electricity market enterprises, which relate to transparent and objective conditions of access to consumption and operation of electricity networks, impartial accelerated development of the electricity market to the requirements of the dynamism of the economic system. Reforming the electricity market combines the processes of achieving organizational, economic, technical, social, and environmental goals. However, this combination causes various consequences and risks associated with profit maximization. One of the most critical problems of the multi-system operator's functioning is ensuring its independence and mutual respect for all market participants. For this, the following measures are legally justified: procurement of related services on a competitive basis; transparent information about the available list of related services, their volumes, costs, terms, and requirements for suppliers; formation of accounting of the expense and income component when implementing the centralization of dispatching management from other types of activity; formation of access to the capacity of interstate electrical networks by conducting electronic auctions; submission of the UES development plan to the CMU of Ukraine for consideration, even in the case of inconsistency between the interested authorities; the investment component of the region will grow only under the conditions of reforming the electricity market. However, the processes of introducing a new model of the electric power services market are being formed under tight deadlines, the introduction and adoption of legislative acts, the implementation of organizational and structural transformations, and software approvals; despite everything, the processes of reforming the electric energy services market are an integral attribute of the region's integral investment policy. Keywords: energy market, energy security system of Ukraine, energy system, energy risks, strategic risks.

Multi-type Adjustable Loads Aggregation Control Strategy for Industrial Parks

China’s industrial parks are the concentration areas of industrial enterprises. The aggregate regulation and control of various types of adjustable loads in industrial parks are effective means to achieve the consumption of renewable energy. However, there are many types of adjustable loads, along with varying amounts and response characteristics, which makes it difficult to achieve unified regulation. In view of the regulation and control characteristics of multiple types of adjustable loads in industrial parks, the regulation and control models of short-term interruptible load, reducible load, translatable load, transferable load and distributed energy storage are established respectively in this paper, aiming at minimizing the regulation cost and maximizing the benefits of the regulation and control auxiliary services, and the aggregate regulation and control strategy of multiple types of adjustable loads in industrial parks is proposed based on the electricity market environment to support the high proportion of renewable energy consumption of the new power system. At the same time, it will obtain auxiliary service income for industrial users. The simulation results of the example show that the proposed strategy reduces the power consumption cost of industrial users and effectively increases the usage of renewable energy without affecting the production of enterprises.

Robust Optimal Scheduling of Integrated Energy Systems Considering the Uncertainty of Power Supply and Load in the Power Market

The integrated energy system is a complex energy system that involves multi-stakeholder and multi-energy coordinated operations. The key to improving its scale and sustainable development is to construct a better-integrated energy system dispatching method which is suitable for the power market. However, the randomness of the supply side and load side of the integrated energy system brings further challenges to system planning and scheduling. Therefore, the optimal scheduling method of an integrated energy system considering the uncertainty of supply and demand in the market environment is studied in this paper. Firstly, the uncertainty models of the supply side and load side of the integrated energy system are established. Then, the optimal scheduling model based on robust chance constraint is established. The reserve capacity constraint is set as a chance constraint with a certain confidence level to maximize the system profit in the power market. Finally, simulations show that the proposed method not only guarantees the robustness of the system but also improves the economy of the system. The method provides ideas for exploring the development mechanism and strategy of integrated energy systems in the electricity market environment.

Day-Ahead and Intra-Day Optimal Scheduling Considering Wind Power Forecasting Errors

The aim of this paper is to address the challenges regarding the safety and economics of power system operation after the integration of a high proportion of wind power. In response to the limitations of the literature, which often fails to simultaneously consider both aspects, we propose a solution based on a stochastic optimization scheduling model. Firstly, we consider the uncertainty of day-ahead wind power forecasting errors and establish a multi-scenario day-ahead stochastic optimization scheduling model. By balancing the reserve capacity and economic efficiency in the optimization scheduling, we obtain optimized unit combinations that are applicable to various scenarios. Secondly, we account for the auxiliary service constraints of thermal power units participating in deep peak shaving, and develop an intra-day dynamic economic dispatch model. Through the inclusion of thermal power units and energy storage units in the optimization scheduling, the accommodation capacity of wind power is further enhanced. Lastly, in the electricity market environment, increasing wind power capacity can increase the profits of thermal power peak shaving. However, we observe a trend of initially increasing and subsequently decreasing wind power profits as the wind power capacity increases. Considering system flexibility and the curtailed wind power rate, it is advisable to moderately install grid-connected wind power capacity within the power system. In conclusion, our study demonstrates the effectiveness of the proposed scheduling model in managing day-ahead uncertainty and enhancing the accommodation of wind power.

Technical-economic feasibility analysis of a large-scale parabolic trough collectors solar power plant in Brazil

This study aims to present the results of the application of a computational tool developed to perform a technical-economic feasibility analysis of large-scale Parabolic Trough Collectors (PTC) solar power plants in Brazil. The gross revenue from cash flow is obtained through an energy trading model for the Regulated Contracting Environment of the Brazilian Electricity Market. The economic viability analysis uses the Net Present Value, Modified Internal Rate of Return, Discounted Payback, and Levelized Cost of Energy indicators. The parametric structural optimization method is used to optimize the technical parameters of the plant. A case study was conducted to analyse the feasibility of implementing a 100 MW PTC in the five regions of Brazil. The results indicated that the best region in Brazil to receive this technology is the northeastern region. However, the project is economically unfeasible in all cities analysed. Therefore, a sensitivity analysis of the main parameters that affect the project's net present value is carried out to evaluate the impact of the economic parameters on the economic viability of an optimized PTC plant located in the city with the best technical performance. An optimistic scenario was evaluated considering the system's response to the parameters investigated in the sensitivity analysis. For this scenario, the results pointed to the project's economic viability.

Linear Fresnel reflector technology in Brazil: a techno-economic evaluation

This study aims to present the results of applying a computational tool developed to perform a technical-economic feasibility analysis of a large-scale Linear Fresnel Reflector (LFR) solar power plant. The viability analysis indicators were obtained considering that the electricity produced trades at the Regulated Contracting Environment of the Brazilian Electricity Market. The parametric structural optimization method is used to optimize the technical parameters of the plant. A case study was conducted to analyze the viability of Implementing a 100 MW LFR power plant in the five regions of Brazil. The results indicated that Northeastern is the best region to install this power plant. However, the project is not viable in all cities analyzed. A sensitivity analysis (SA) of some strategically selected variables was conducted to evaluate its impacts on the project's net present value of an optimized LFR plant located in the city with the best technical performance. Finally, an optimistic scenario was evaluated considering the net present value's response to the parameters investigated in the SA. For this scenario, the results indicated the project's economic viability.

Preface

These proceedings are based on the program of the workshop dedicated to New Energy System and Electrical Engineering. Understanding the path from New Energy Systems to Electrical and Power Engineering will involve a strong interdisciplinary interaction among experts in theory and applications. In this workshop a particular attention was devoted to applications in New Energy System and Electrical Engineering.2022 4th International Conference on New Energy System and Electrical Engineering (NESEE 2022) took place virtually in Chongqing, China from December 9th to 11th, 2022. It can be expected that with the recent developments in the fields towards more user-friendly new energy system and electrical engineering, new energy and electrical will become a vital link in the chain of sustainable development and highly automation in the near future.Many scientists and researchers all over the world have contributed to the emerging technology of new energy and electrical engineering. At this meeting we had a comprehensive overview of these fascinating fields and of future scenarios thanks to the participation of leaders of the most important projects. Fushuan Wen, a renowned professor from Zhejiang University, China, made a keynote speech and shared his research Restoring a Faulted Power System Having Rich Renewable Energy Generation in Electricity Market Environment with us. His research domains include: a) power economics and electricity markets; b) power system investment, planning and operation optimization; c) smart grids and electric vehicles; d) power system alarm processing, fault diagnosis and system restoration; e) artificial intelligence.The plenary sessions covered a broad range of topics, as we considered it important to promote communication between the communities pursuing research in different areas of new energy and electrical engineering. The topics discussed included Energy Saving Technologies, Energy Storage Technologies, Thermal and Power Engineering, Electrical Automation and Power Engineering, Smart Grid / Power IC, Power Machinery and Engineering, etc.The NESEE conferences in the past have proven to be an important forum at which energy scientists, chemists, electrical specialists brought together a critical mass of thoughts, findings and considerations and have been endorsed by many international and national energy and electrical organizations and publishers.We hope that this NESEE 2022 conference has been a useful contribution to the fields of new energy and electrical engineering, and that it may encourage the organization of subsequent NESEE conferences on these diverse, challenging and fascinating fields.Last but not the least is our acknowledgement. We would like to thank all the participants, especially those who contributed speeches, posters and manuscripts, for making NESEE 2022 such an exciting and memorable conference. We thank the Technical Program Committee members, for putting together an outstanding program. We gratefully acknowledge the support of editors and members of the Journal of Physics: Conference Series, for their efforts in making this volume published.The Committee of NESEE 2022List of Committee Member is available in this Pdf.

Analysis of strategic bidding of a DER aggregator in energy markets through the Stackelberg game model with the mixed-integer lower-level problem

An open electricity market environment provides sufficient conditions for a distributed energy resource (DER) aggregator to participate in the day-ahead electricity market. Therefore, a one-leader multi-follower Stackelberg game model is developed to analyze the strategic bidding behavior of a DER aggregator and one of the lower-level problems is to check the security of the distribution system (DS), which is elaborated by the mixed-integer second-order cone programming (MISOCP) model considering the active power loss and discrete controls in the DS. Moreover, we first use the Karush–Kuhn–Tucker (KKT)-based reformulation approach to transform the model into a one-leader one-follower model. However, the objective function of the upper-level model cannot be completely linearized by using the strong dual theorem because the active power loss of the DS is considered in the lower-level security check problem. Therefore, we propose a piecewise linear approximation approach based on the data-driven algorithm to linearize the upper-level objective function. Moreover, we apply multivariate linear surrogation to transform this one-leader one-follower model into a single-level MISOCP model. Finally, we verify the correctness of the proposed algorithm by comparing it with the particle swarm optimization (PSO) algorithm in a small integrated transmission and distribution (T&D) system and validate the effectiveness of the proposed algorithm in a practical integrated T&D system.

Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization

In the pilot provinces of China’s electricity spot market, power generation companies usually adopt the separate bidding mode, which leads to a low willingness of demand-side response and poor flexibility in the interaction mechanism between supply and demand. Based on the analysis of the demand response mechanism of the power day-ahead market with the participation of power sales companies, this paper abstracted the game process of the “power grid-sales company-users” tripartite competition in the electricity market environment into a two-layer (purchase layer/sales layer) game model and proposed a master–slave game equilibrium optimization strategy for the day-ahead power market under the two-layer game. The multi-objective multi-universe optimization algorithm was used to find the Pareto optimal solution of the game model, a comprehensive evaluation was constructed, and the optimal strategy of the demand response was determined considering the peak cutting and valley filling quantity of the power grid, the profit of the electricity retailers, the cost of the consumers, and the comfort degree. Examples are given to simulate the day-ahead electricity market participated in by the electricity retailers, analyze and compare the benefits of each market entity participating in the demand response, and verify the effectiveness of the proposed model.

Flexible load optimal regulation strategy of commercial buildings under the environment of electricity market

Under the electricity market environment, the flexible load of power users can not only reduce the cost of power consumption by adjusting their own power consumption behaviors according to the real-time price (RTP), but also can obtain additional benefits by participating in auxiliary services. The load of commercial buildings (CBs) is concentrated and makes a greater contribution to the peak load of the power system. If the flexible load of CBs can be regulated, it has great benefits for shifting the peak load of the power system in addition to reducing the electricity cost. On the basis of analyzing the characteristics of flexible loads in CBs, a flexible load regulation model including short-time interruptible load (IL), reducible load (RL), translatable load (TL), air conditioning system (ACS) and energy storage system (ESS) is established. Considering the electrical comfort and temperature preference, the objective function is jointly constructed with the electrical cost, temperature discomfort and ancillary service income. An optimal regulation strategy for multiple types of flexible loads in CBs based on the electricity market environment to realize the economic power consumption of CBs is formulated. The simulation results show that the proposed strategy can reduce the power cost of CBs and lower the peak demand under the condition of satisfying the power consumption comfort.

Short-term Load Forecasting Based on Multiple PSO-LSSVM under Electricity Market Environment

Affected by the development of the electricity market and many other factors, it is very difficult to obtain high-performance short-term load forecasting methods. The day-ahead load forecasting of the power grid is deeply studied by dividing 24 hours forecast outputs into several groups and optimizing them respectively. As a result, a new short-term load forecasting method is proposed by considering the load variation trend in different periods. First, through the historical load variation trend analysis, the 24 hours load forecasting output is divided into several sets. Then, the prediction algorithm combining multi-group particle swarm optimization and least squares support vector machine is proposed. Finally, the improved algorithm and the original particle swarm optimization, and the least squares support vector machine algorithm are used to predict the short-term load data of Jiangxi Province. The experiment results show that the improved algorithm can identify the load characteristics of a different time with high prediction accuracy, which has practical application value.

Aggregator-induced demand response and bidding strategy for electricity market environment based on Ethereum smart contracts

ABSTRACT With the liberalization of the Electricity Market (EM), a secure demand response program (DRP) for real-time decisions is needed to enhance the effectiveness, stability, and security of the EM. Customers play a crucial role in DRP as they can offer flexibility when the market demands it. Nevertheless, the aggregator does not fully understand the customer’s available DR capacity to accurately bid on market transactions. To do so, the article presents a smart contract based on an optimal bidding strategy for an aggregator that considers customer receptiveness. First, we endeavor to determine the customer’s receptiveness concerning various incentives where the home energy management system (HEMS) introduces the employed load adjustment for electrical appliances. Second, the aggregator adopts a rational approach to employing the optimal day-ahead bidding strategy and scheduling for energy storage systems (ESS) to exploit economic benefits. The execution of Ethereum smart contracts in market transactions ensures transparency, security, and superior reliability. A test case with realistic data is presented to demonstrate the usefulness of the proposed bidding strategy. The results confirm that the aggregator determines the optimal bidding value for the next day. An aggregator could generate a profit of $23 from the bidding strategy. The regional transmission system operator delivers 485 kWh of flexibility, of which consumers account for 60% and ESS for 40%, generating a profit of $40 per day. Prosumers selling electricity to the aggregator would make an average profit of $6 per day. Thus, the execution of DRP and a bidding strategy results in economic benefits for the aggregator and other entities in the EM.

Operation Cost Optimization Method of Regional Integrated Energy System in Electricity Market Environment Considering Uncertainty

In the electricity market environment, the regional integrated energy system (RIES) can reduce the total operation cost by participating in electricity market transactions. However, the RIES will face the risk of load and electricity price uncertainties, which may make its operation cost higher than expected. This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty. Firstly, based on the operation cost structure of the RIES in the electricity market environment, the energy flow relationship of the RIES is analyzed, and the operation cost model of the RIES is built. Then, the electricity purchase costs of the RIES in the medium- and long-term electricity markets, the spot electricity market, and the retail electricity market are analyzed. Finally, considering the risk of load and electricity price uncertainties, the operation cost optimization model of the RIES is established based on conditional value-at-risk. Then it is solved to obtain the operation cost optimization strategy of the RIES. Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy, constrain the risk of load and electricity price uncertainties, and help balance the risks and benefits.

Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

Application of blockchain technology in autonomous electricity transaction and settlement at the end of distribution network

Abstract With the deepening of the concept of low-carbon environmental protection, the use of renewable energy in social production has become more and more popular. More and more consumers in the distribution network have begun to change their identities to become consumers with the continuous reform of the electricity sales side. Electricity trading and settlement methods in the electricity market directly affect the relevant income of market entities, so a fair and reasonable settlement mechanism is an important factor for the normal operation of the electricity market. Electricity trading refers to the buying and selling of electricity commodities and services, including electricity trading, auxiliary service trading, and transmission rights trading. With the expansion of the electricity market, how to deal with the deviation of electricity is also a difficult problem that requires electricity settlement. This paper aimed to study the application of blockchain technology in the autonomous electricity transaction and settlement at the end of the distribution network. It was expected to use the blockchain technology to set up related transaction mechanisms, provide new solutions for electricity transaction and settlement, and improve the efficiency and fairness of electricity settlement. Blockchain technology does not rely on additional third-party management agencies or hardware facilities, and there is no central control. Except for the self-contained blockchain itself, through distributed accounting and storage, each node realizes information self-verification, transmission and management. On the basis of sorting out the principles of electricity balance and electricity price formation, this paper proposed a contract decomposition method with a higher degree of fit, and revealed the settlement principle of deviation electricity in the electricity market environment. Aiming at the application scenario of blockchain in the energy Internet, an energy trading platform based on blockchain was designed, and the test results showed the feasibility of blockchain in electricity settlement scenarios. The experimental results of this paper showed that in the traditional electricity purchase fee settlement, the average transaction price is stable at 0.6 yuan/KWh. In the blockchain settlement process, the electricity purchase is 25 yuan when the electricity purchase is 50 KWh. Therefore, blockchain settlement is used more frequently in the market, and the profits are higher.

Retailing Strategies for Power Retailers with a Generator Background Considering Energy Conservation Services under the Internet of Things.

Facing the electricity market environment, in which the traditional power grid is transformed into a smart grid, power retailers with a generator background are designing new business models of cold-heat-electricity multi-energy supply based on the Internet of Things data collection, interconnection, computing and other technical supports. On the other hand, through internet of things real-time monitoring technology, the necessity of setting up energy security for power retailers is explored to enhance the control’s ability to deal with the risks of electricity sales. Firstly, based on internet of things data analysis, retail strategies such as cooling-heat-electricity multi-energy packages, desulphurization and carbon emissions and energy conservation are designed. Then, a revenue cost measurement model based on the generator background of the power retailers is established. A source of data for the expansion of power retailers and the proliferation of load users is provided through the real-time monitoring of new business models that consider the operation of energy conservation on the supply and use side. Finally, an analysis based on the detection of operation under the scenarios constructed in the example of coal price market fluctuations and proliferation stagnation of user-side packages is conducted. It is verified that the power retailers with a generator background can effectively weaken the adverse impact of upward fluctuations in the coal price market in the peak season of energy consumption on the cost of power retailers by setting energy conservation. At the same time, the diffusion of a new business model in the user side is improved, and the revenue source of power retailers is further expanded. Therefore, taking energy conservation as an important innovation technique of retail strategy can enhance the market competitiveness and risk control ability of power retailers.