Electricity Spot Market Research Articles

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.

A new demand response management strategy considering renewable energy prediction and filtering technology

Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage systems. More importantly, the power generation prediction is of great significance to the demand response management (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the Whale Optimization Algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable resources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable reference for future work.

A novel load-side settlement mechanism based on carbon emission flow in electricity spot market

In the electricity spot market (ESM), thermal power plants (TPP) are quoted based on fuel costs and carbon costs, which impose carbon costs from the generation side to the load-side. The cost of load-side participation in the ESM will increase further. In addition, the traditional ESM centralized bidding model is based on the marginal clearing price, without considering the line transmission constraints, which may affect the security of the power system. To solve this challenge, firstly, an ESM centralized bidding clearing model including the generation side and the load-side is established based on the transaction mechanism of centralized bidding in the ESM. In this model, the marginal clearing prices of two possible bidding scenarios in the ESM are considered. Secondly, the marginal clearing price of the ESM is designed based on the security constraint of the power flow, which can avoid power transmission exceeding its capacity. Finally, a carbon emission cost settlement mechanism is proposed based on the carbon emission flow (CEF) method, which can reduce the carbon emission cost paid by the load-side. The effectiveness of the method is verified by simulations. Compared with a traditional load-side settlement model, the settlement cost on the load-side is reduced by 30.45 %.

Empirical evidence for the edge of a centralized regional market over a cross-province balancing market in allocating electricity resources: A case study of Yunnan in China

Alongside China’s new round of electricity reform, Yunnan has implemented medium- and long-term market reform in the electricity sector since 2015 with little progress seen in establishing an electricity spot market. To this end, this paper attempts to identify the pathway to an electricity spot market for Yunnan through its realities and lessons learned from Europe. As such, on the ground of the status quo of the energy mix, generation features, load structure and profiles, power supply–demand situations, and market progress in the Yunnan electricity system, this paper reviews two classical market frameworks commonly adopted worldwide, from the perspectives of market organizing and the core optimization model broadly used in coordinating system operation and clearing the market. Then the contrast of Yunnan with the European experience is analyzed with respect to the energy mix, generation, transmission, and exchange, as well as the political and economic institutional arrangements, market conditions, and driving forces. Moreover, the Yunnan case study scenarios and likely consequences and benefits under different market frameworks are further analyzed to verify the edge of a centralized optimal-power-flow-based regional market over a decentralized cross-province balancing market, in productively reducing price distortion, allocating resources, maintaining power supply–demand balance at all times, and promoting renewable energy integration across a wider region. Finally, policy suggestions concerning the pathway to an electricity spot market are provided for Yunnan in further deepening its electricity reform.

The equilibrium analysis and potential modifications on the China pilot electricity spot market

The rapid development of the electricity industry reform in China has been witnessed in recent years, and one of the iconic achievements could be the establishment and trial operation of the electricity spot markets. However, even though the market clearing model for electric energy is identical to that adopted in the mature markets in the U.S., which has been widely confirmed to respect the principle of incentive compatibility, some special features are instead observed regarding the offering behaviors of generators, indicating the inconsistency with the principle of incentive compatibility. Therefore, an in-depth study on the causes for the observed features is of urgent significance, and corresponding potential modifications on the market mechanism are anticipated. This paper first establishes an equilibrium analysis model for the electric energy and frequency regulation ancillary service (FRAS) markets, based on the real-world practice in China. By providing the optimal offering strategies of the generators at the equilibrium point, the equilibrium analysis could shed light on the causes for the observed features of the offering behaviors. Then, the paper proposes potential modifications on the market mechanism considering the real institutional and operational characteristics in China, with proposing a multi-objective optimization model and a decentralized algorithm for market clearing, as well as giving full consideration to the availability of FRAS. Finally, the feasibility and effectiveness of the proposed model and algorithm are justified by case studies. The results demonstrate that the proposed modifications could achieve the identical objective of realizing the minimum system costs as the existing market mechanism adopted in the U.S., and be more adaptive to the real institutional and operational characteristics in China. Policy recommendations are also presented for the establishment and operation of the electricity spot markets in China.

Security requirement classification of electricity trading data based on hierarchical fuzzy Petri network

With the emergence of spot electricity trading, market-oriented trading has been intensively carried out, forming a multi-cycle trading system. In this process, a large amount of fine-grained electricity trading data circulates on the trading platform. Trading data is an important basis for decision-making in the electricity spot market, which directly affects the trading profits of market entities, proper disclosure of this information is very important for market enterprises. Information disclosure must ensure the validity and security. However, it is hard to judge the security demand for trading data, and there is no suitable evaluation system for determining the security requirement of data, which will limit the electricity market’s further development. In this paper, we first design an indicator system for security requirements classification, which manages data risks from three aspects: data classification, data risk, and entity demand. This system will guide us in determining data security requirements and further help identify differentiated data security protection schemes. Then, based on this system, we propose the classification method of data security requirements through a hierarchical fuzzy Petri net. The lower network realizes a reasonable assessment of data risk with reference to the index system, and the upper network finally determines the level of security requirements through the fuzzy rule base. Last, two types of data are selected to judge their security requirements. The results show that our method can provide a reference for privacy protection in electricity market data.

A safety check method to maximize the effective reserve by optimizing the power of the tie-line in the power market

To ensure the stability of the electricity spot market and the safety of the provincial and regional power systems, a safety check method is proposed to maximize the effective reserve resources in the power system by optimizing the power of each tie-line. This safety check method accurately models the tie-line equipment and the effective reserve resources and is coupled with each constraint of the electricity spot market clearing model to form a safety check algorithm to optimize the power of tie-line power. The model involved in this paper is a linear model, which has a clear implementation method in practical dispatching applications. Through this method, the power configuration scheme of each tie-line to meet the electricity spot market constraints can be obtained, and the safety check results have the executability of the power market. The rationality and feasibility of the safety check algorithm results are verified by simulating the provincial-scale electricity spot market. According to the simulation results, this method can release effective reserve resources and provide more guarantees for the safe operation of the power grid. In addition, this method can save up to 4.9% of the total operation cost of the power system and improve the dispatching economy of the power system. This method is of great significance to ensure the safe operation of the power system and the day-ahead market and real-time market scheduling in the actual power spot system. In addition, this method also has great guiding significance for the analysis of the actual reserve situation of the power market after the event.

Multi-interval settlement system of rolling-horizon scheduling for electricity spot market

Multi-interval settlement is conducive to meeting the needs of growing renewable energy resources with great intermittency and volatility and managing the effective operation in the electricity spot market. However, the insufficient incentive of market price inaccurately reflecting the total cost of the electricity spot market caused by the inaccuracy of generation and load prediction in the current multi-interval settlement will lead to inefficient market scheduling, causing the market participants to deviate from dispatch instructions. Based on the problem above, a new multi-interval settlement system of rolling-horizon scheduling including the period selection of look-ahead schedules and enhanced settlement mechanism is proposed to improve the price incentive for the electricity spot market. The proposed multi-interval settlement system can produce a better look-ahead period and a more economically efficient dispatch solution inducing dispatch-following incentives. A numerical example shows that the proposed multi-interval settlement system outperforms the traditional settlement mechanism regarding economic efficiency.

Sizing a hybrid hydrogen production plant including life cycle assessment indicators by combining NSGA-III and principal component analysis (PCA)

In the design and synthesis of energy systems, mathematical tools such as optimization algorithms are often used. While using them, environmental indicators are increasingly used as optimization criteria to exploit the possible environmental benefits of these systems. The problem is that the high number of environmental indicators poses a problem for optimization algorithms in terms of convergence, computational time and visualization. In this paper, this problem is addressed through a many-objective search of solutions using a state-of-the-art evolutionary algorithm, NSGA-III. Furthermore, the performance of this algorithm is tested using different settings in the PCA-based objective reduction framework. The original 14 indicators are reduced to seven, four, three and two, which reveal important insights about the use of NSGA-III, objective reduction and a combination of the two. It was found that by using objective reduction, the performance of NSGA-III can be further improved in terms of the quality of solutions and computational time. However, beyond a certain point, further objective reduction leads to a trade-off between solution quality and computational time. For this case study, the best quality solutions were obtained in the PCA reduction procedure when the CUT value was maintained at 99.99% without additional reduction in the last step, using a correlation matrix. The algorithms were applied to a real-life sizing case study involving hydrogen production from polymer-electrolyte-membrane (PEM) water electrolysis, for which the demand is furnished by the electricity spot market, solar photovoltaics (PV) or wind turbine in Marseille, France. These results will be useful for future applications of many-objective optimization and objective reduction. They will also be practical for including environmental indicators in the many-objective search for solutions.

Energy Trading Strategy of Distributed Energy Resources Aggregator in Day-Ahead Market Considering Risk Preference Behaviors

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China’s power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China’s power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market price fluctuation, the robust decision model and opportunity decision-making model in the day-ahead market are constructed, respectively. Finally, to deal with the irrational behavior of the DERAs’ perception of “gain” and “loss” with market risks in China’s two-tier market environment, the prospect theory and the marine predator’s algorithm (MPA) are employed to obtain a day-ahead trading decision scheme for DERA. The analyses show that RDES with robust preference can withstand greater price volatility in the day-ahead market; they will reduce the bidding expectations and increase the system operating cost to improve the achievability of the expected revenue. However, DERAs under the opportunity strategy is more inclined to sell electricity to the market and offset system operating costs with revenue. The proposed model can provide strategic reference for DERAs with different risk preferences to bid in day-ahead market and can improve the level of aggregators’ participation in electricity trading.

Distributed Feature Selection Considering Data Pricing Based on Edge Computing in Electricity Spot Markets

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

Mechanism and Method of Auxiliary Decision-Making for Dispatching Real-time Operation in Electric Spot Market

Starting from the market rules of the pilot provinces of the domestic spot market, this paper analyzes the mechanism of market intervention and suspension, the mechanism of the power supply period, and the mechanism of market clearing anomaly. At the same time, combined with the real-time operation of the Inner Mongolia power grid, the traditional dispatching mode and the power spot market operation dispatching mode are compared. Given the abnormal frequency, section overload, and insufficient spinning reserve in real-time operation, an auxiliary decision mechanism of manual intervention in the power spot market is proposed. The mechanism includes load bias setting, section limit bias setting, deviation monitoring, and other auxiliary functions, which provide a method of quick intervention under the electric spot market to ensure the safe and stable operation of the power grid.

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.

Guarantees of Origin and Competition in the Spot Electricity Market

Guarantees of Origin and Competition in the Spot Electricity Market

Discussion on the innovative mode of charge management under the situation of electric power marketization reform

The development of electricity spot market has greatly increased the workload of the work of charging and collecting. Charge and collect work is a very important part in the daily operation of power supply enterprises. The quality of the work will affect the economic benefits of the power supply enterprises to a certain extent, and even relates to the resources and energy security of the country. Therefore, only by strengthening the management of charge and verification, optimizing the process of charge and verification, and innovating and upgrading the management mode, can the quality of charge and verification work be improved to a certain extent and enterprises be helped to obtain greater economic benefits. This article is mainly to analyze the power supply enterprises under the situation of the reform of the power market charge and check the work process and the power supply enterprises to charge and check the work of management innovation mode.

Spot Electricity Market Design Considering Carbon Emissions for a Power System With a High Penetration of Renewable Energy Generation

Spot Electricity Market Design Considering Carbon Emissions for a Power System With a High Penetration of Renewable Energy Generation

Cost Diversion Strategies for Pumped-Storage Tariffs for New Power Systems

Pumped-storage plants are the most significant electrical storage component in new power systems and show great potential for scaling up. In this paper, economic costs and benefits have been investigated. Both the costs and benefits can be divided into transmission and distribution tariffs; however, various factors need to be considered to reduce costs in transmission and distribution tariffs. The cost characterization methodology for pumped-storage power plants has been developed. A mathematical model for dispersal through the medium and long-term electricity market, the electricity spot market, the ancillary services market, and the leasing of capacity to scenic power stations has been proposed. A case study has been discussed for a regional grid company and a provincial grid company; also, the effectiveness of dispersal has been analyzed based on the case study. The results indicate that when the spot market and the capacity leasing of new energy sites play the largest role in the diversion, the transmission and distribution prices in Province A can be controlled within 1 cent/kWh by applying various diversion methods.

Optimization Model of Electricity Spot Market Considering Pumped Storage in China

As China’s electricity market continues to evolve, pumped hydro storage will participate in electricity spot market transactions. According to the latest price policy of pumped storage, pumped storage units will not participate in the spot market bidding for a long time and will be settled at the spot price. Aiming at maximizing the welfare of the whole society, this paper proposes a full dispatch model for pumped storage units to participate in China’s electricity spot market under the new power system. By transforming the day-ahead spot market clearing results into the boundary conditions of the real-time spot market, the model proposed in this paper realizes the fine adjustment of pumped storage units’ operation curve in the real-time market and further improves social welfare.

The impacts of carbon pricing on the electricity market in Japan

To achieve carbon neutrality by 2050, Japan should speed up reducing fossil fuel reliance on production, especially for energy-intensive sectors. One way is by implementing a carbon pricing system, converting emissions from fossil fuels to costs of production and consumption. This study focuses on the correlation between the price of wholesale electricity spot market and carbon cost of nine regions in Japan through carbon cost pass-through rate. This paper applies polynomial OLS regression with degree two through machine learning technics to better fit the relationship between electricity price and demand and also applies a generalized additive model to capture the nonlinear relationship between fuel spread and carbon cost and test the robustness of estimated CPTR. The results show that Hokuriku, Kyushu, Shikoku, Tohoku, and Tokyo have a lower value carbon cost pass-through rate while Kansai, Chubu, and Chugoku have a higher rate of carbon cost pass-through. There is a special case in Hokkaido as the negative relationship between electricity price and carbon cost. Those findings are also crucial in supporting future policy adjustments.

A game theoretic technique for risk-based optimal bidding strategies in energy aggregators of markets: Knowledge management approach

The advent of green energies contributes to their high penetration in the energy sector. It could be guessed from the beginning that societies would so excited about the advantages of these energy sources to deploy in the infrastructure of energy systems even before deep investigation about their possible disadvantages. Unfortunately, the management of these sources has become challenging due to the uncertainty associated with environmental factors which can make social, economic, and technical issues. On the other hand, new emerging technologies such as electric vehicles (EVs) have helped much to reduce air pollution and thus make positive effects on human life. Still, we all accept that new technology can make the problem structure much more complicated. This article proposes an innovative management approach for risk-based optimal bidding strategies of energy aggregators in the energy market considering green sources. Moreover, it proposes a new strategy for the bidding offer strategy for green energy aggregators in the spot electricity market. An innovative bi-level optimization approach is developed for the maximization of the profit of the non-participatory companies considering all the technical and social limitations. The equilibrium market spots in this method are calculated as per the Nash equilibrium rule. In such a system, all producers and consumers are considered market players to propose the EV's behavior and as a result, provide a more competitive environment. The results clearly show the high performance of the proposed model for energy management in a green social and economic framework.