Electricity Market Operation Research Articles

Economic dispatch of microgrid generation-load-storage based on dynamic bi-level game of multiple stakeholders

During the participation of microgrid operators(MGO) and shared energy storage investors(SEI) in electricity market operations, unclear positioning of shared energy storage(SES) in the trading mechanism leads to decision-making limitations and mutual pricing constraints among stakeholders. Therefore, a dynamic bi-level game model based on multiple stakeholders is established in the paper. Considering the full life cycle investment costs, leasing costs, and load peak-shaving ancillary service revenue, establish a net revenue model for SEI. In the upper level, with SEI as leaders and MGO as followers, establishing a master-slave game model for optimizing SES leasing prices, and formulate leasing strategies. In the lower level, with MGO as leaders and cluster users as followers, establishing a master-slave game model for optimizing electricity prices and formulate scheduling strategies. A hybrid multi-strategy improved whale optimization algorithm based on simulated annealing(HSWMO) is proposed, combined with the theory of fixed points to solve the optimization problem of the upper and lower levels until obtaining the optimal solution that converges to a fixed point. Through case analysis, it is verified that the operational mode of SES as a market entity can bring higher economic benefits to multiple stakeholders such as MGO and cluster users. Meanwhile, the analysis from the perspective of SEI examines the optimal capacity investment and payback period for SES, providing effective reference for the comprehensive decision-making of SEI and MGO.

Dynamic capacity withholding assessment of virtual power plants in local energy and reserve market

The increasing utilization of distributed generation resources led to the formation of active distribution networks and virtual power plants (VPPs), which have changed the paradigms of electrical energy transactions in local energy markets. The VPPs can form capacity-withholding groups and impose market power to gain more profits, which may increase the costs of energy procurements for consumers. This paper presents an algorithm for the local electricity market operator in distribution networks to assess the dynamic capacity withholding of VPPs in the local energy and reserve markets. The main contribution of this paper is proposing indices to evaluate the dynamic capacity withholding of VPPs in energy and reserve markets. The other contribution of this paper is that it also quantitatively analyzes the impact of withholding processes on the flexibility of the distribution network. An optimization process is used to estimate coordinated offers of VPPs in the energy market in order to prevent the formation of withholding groups. The proposed algorithm was assessed for the 123-bus IEEE test system and the energy and reserve dynamic capacity-withholding indices were determined for different operating conditions.

Electricity Price Analysis for Renewable Energy Dominated Electricity Markets under Extreme Scenarios

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances.

The potential of publicly available weather forecasts for market operations in aggregated photovoltaic plants

Uncertainty in solar generation forecasts affects electricity market operations, forcing producers to bid conservatively and, consequently, reducing the amount of solar energy fed into the grid. This study analyses the potential of aggregated forecasts to reduce the errors of energy generation in medium-sized PV plants. To this objective, publicly available forecasts from the Global Forecasting System were processed with a basic neural network to generate hourly energy forecasts for individual PV plants of 1.1 MW and for the aggregated production of three of them (3.3 MW). The validation was carried out considering typical day-ahead and intraday market horizons, two zones with different atmospheric complexity, and discerning three types of days in terms of the received irradiation. Results show that the aggregation of forecasts would be beneficial as a general strategy in all cases, but especially in the zone of complex atmospheric dynamics and for the day-ahead horizons (36-42 h).

Optimization model of monthly continuous bidding transaction in electricity market considering risk hedging

In order to further alleviate the problems of large assessment deviations, low efficiency of trading organisation and difficulties in system optimisation in medium- and long-term market trading, the article proposes an optimisation model for continuous intra-month bidding trading in the electricity market that takes into account risk hedging. Firstly, the current situation of market players’ participation in medium and long-term trading is analysed; secondly, the impact of contract trading on reducing operational risks is analysed based on the application of hedging theory in the primary and secondary markets; finally, the continuous bidding trading mechanism is designed and its optimisation effect is verified. The proposed model helps to improve the efficiency of contract trading in the secondary market, maintain the stability of market players’ returns and accelerate the formation of a unified, open, competitive and well-governed electricity market system.

The carbon tax and the crisis in Australia’s National Electricity Market

Australia faced a severe energy crisis in 2022, which prompted the Australian Energy Market Operator to halt in National Electricity Market operations from June 15 to June 24. In this study, which examines half-hourly data from Australia’s National Electricity Market between 2010 and 2016, we aim to demonstrate the notable and intended consequences of the carbon tax, which was enacted in 2012 and repealed in 2014. Our findings show that this tax had a significant impact on wholesale prices. It also induced substitution effects in the generation mix. If these effects had persisted, the electricity generation landscape in 2022 would have been different. Specifically, the economic feasibility of coal would have decreased and that of gas would have increased. As a result, the composition of electricity generating capacity leading up to the 2022 crisis would have been altered.

Construct watchlist security constraints for monthly unit commitment considering wind power uncertainty

AbstractMonthly unit commitment (UC) is a decision‐making process that determines the optimal generation scheduling of mid and long‐term power system, often operating as a subproblem within monthly electricity market operations and long‐term maintenance scheduling optimization in China. Dealing with transmission constraints in monthly security‐constrained unit commitment (SCUC) is considered to be challenging, but its simplification can be achieved through eliminating inactive security constraints. Additionally, the uncertainty associated with wind power needs to be given due consideration in monthly scheduling. Here, a three‐step watchlist construction strategy is proposed to facilitate the rapid screening of overloaded power flow constraints in monthly unit commitment, including three lists (i.e. list of risk, list of concern, and list of interest) under different conservatism. Importantly, the shift factor method is utilized to screen the potential overloaded constraints arising from the redispatch process for hedging the effects of large wind uncertainties. Results obtained from the testing of IEEE‐118 system indicates that more than 99% of active power flow constraints could be successfully screened out when wind power fluctuation is at 20%. As such, the proposed scheme provides an effective method to consider uncertainty associated with large‐scale wind power generation integrated into the power grid.

Model of integrated pool/conventional/alternative electricity market operation using pay-as-bid pricing

A pricing model considering the simultaneous interaction of pool, conventional and alternative markets in a power system is presented. This work inserts a competition for alternative power plants separating the pricing for these three markets. The model use the ‘Pay-as-Bid’ (PAB) pricing approach in opposite of classical marginal pricing (MP) approach because the inelastic demand characteristic and the necessity of reduce total operation cost. In the model pricing approach, an integration process involves an AC Optimal Power Flow (OPF) for obtaining awarded bids in the pool and market with the presence of conventional or alternative energy bilateral contracts. As a result, the obtained prices of pool, conventional and alternative energy incorporate the influences of the topology, voltage levels, losses and capacity limits of generators and transmission lines. Results show that agents can plan their portfolios based on reasonable stable prices that reflect the impact of supplying several electricity types and associated costs of resources in several operation scenarios and bid strategies. From the perspective of the system regulator, the minimization of payments by PAB ensures the supply of energy, transmission losses and alternative energy requirements as well as enforcing financial adequacy. Numerical cases are presented for evaluating the model

The hidden costs of electricity

The price of electricity must take into account all the costs, both direct and induced, which are often hidden. This is true when calculating the average production cost per kWh at the power station. It is also true in the context of the operation of the wholesale electricity market based on the merit order, which leads to power stations being called on the basis of the marginal cost per kWh. Ignoring these hidden costs leads to distortions of competition and sub-optimal choices.

Analysis of Risk Characterization and Identification Techniques for Electricity Spot Settlement Based on Multilayer Perceptron Machine

Abstract In this paper, the structural characteristics of the perceptron neural network and the calculation method of the hierarchical relationship of the MLP neural network model are first studied. Then the aspects of two-part settlement, generation-side settlement, and customer-side settlement in the electricity spot settlement mechanism are studied, and the importance of these mechanisms for the operation of the electricity market and risk identification is pointed out. Following that, the effectiveness of the risk identification model is assessed and analyzed. This paper examines market performance indicators, the impact of the dual-track mechanism, and the time characteristics of the price index to characterize risk. The results show that in Guangdong, for example, the price in the day-ahead market is much higher than the supply-demand equilibrium price most of the time, and the maximum difference can be as high as 0.662 yuan/(kW-h). For the entire month, the real-time market’s average price is RMB 0.546/(kW-h) and it is RMB 0.063/(kW-h) higher than the day-ahead market. The importance of this study lies in its role in managing and responding to risks for electricity market operators and participants.

Research on the Construction of Electricity Market Operation Efficiency Assessment Model and Indicator Early Warning Based on Cloud Entropy Optimization Algorithm

Abstract In this paper, we first constructed the indexes for evaluating the operational efficiency of the electric power market, and screened the indexes by using the Delphi method. Then, the basic numerical characteristics of the cloud model are explored, and the hierarchical boundaries of the power market operational efficiency assessment indexes are delineated. The market operational efficiency assessment model is established by using the cloud entropy optimization algorithm, and the objective weights are determined by the entropy weighting method. The market operation efficiency assessment model is used to analyze the operation efficiency of the power market, and targeted suggestions are made to address existing problems. Finally, the risk warning threshold of the operation efficiency of the power market is set. The risk warning of the operation efficiency of the power market is analyzed. The predicted values and expected output values are compared to validate the applicability of the model in this application. The average value of the operation efficiency of the power market is 3.3, the level of efficiency is between 2 and 3, and the power market operation efficiency is in the range of 2-3. The range is 2-3, and the grade of power market operational effectiveness is medium. This study helps improve the operational efficiency of the power industry and optimize the allocation of market resources in the power industry.

Operation and evaluation of digitalized retail electricity markets under low-carbon transition: recent advances and challenges

With the growth of electricity consumers purchasing green energy and the development of digital energy trading platforms, the role of digitalized retail electricity markets in the low-carbon transition of electric energy systems is becoming increasingly crucial. In this circumstance, the research work on retail electricity markets needs to be further analyzed and expanded, which would facilitate the efficient decision-making of both market players and policymakers. First, this paper introduces the latest developments in the retail electricity market under low-carbon energy transition and analyzes the limitations of the existing research works. Second, from three aspects of power trading strategy, retail pricing methodology, and market risk management, it provides an overview of the existing operation and mechanism design strategies of the retail electricity market; then, it provides a systematic introduction to the evaluation system and monitoring methodology of electricity markets, which is not sufficient for the current digitalized retail electricity markets. Finally, the issues regarding operation evaluation and platform optimization of the current digitalized retail electricity market are summarized, and the research topics worth further investigations are recommended.

Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting

Prediction of electricity price is crucial for national electricity markets supporting sale prices, bidding strategies, electricity dispatch, control and market volatility management. High volatility, non-stationarity and multi-seasonality of electricity prices make it significantly challenging to estimate its future trend, especially over near real-time forecast horizons. An error compensation strategy that integrates Long Short-Term Memory (LSTM) network, Convolution Neural Network (CNN) and the Variational Mode Decomposition (VMD) algorithm is proposed to predict the half-hourly step electricity prices. A prediction model incorporating VMD and CLSTM is first used to obtain an initial prediction. To improve its predictive accuracy, a novel error compensation framework, which is built using the VMD and a Random Forest Regression (RF) algorithm, is also used. The proposed VMD-CLSTM-VMD-ERCRF model is evaluated using electricity prices from Queensland, Australia. The results reveal highly accurate predictive performance for all datasets considered, including the winter, autumn, spring, summer, and yearly predictions. As compared with a predictive model without error compensation (i.e., the VMD-CLSTM model), the proposed VMD-CLSTM-VMD-ERCRF model outperforms the benchmark models. For winter, autumn, spring, summer, and yearly predictions, the average Legates and McCabe Index is seen to increase by 15.97%, 16.31%, 20.23%, 10.24%, and 14.03%, respectively, relative to the benchmark models. According to the tests performed on independent datasets, the proposed VMD-CLSTM-VMD-ERCRF model can be a practical stratagem useful for short-term, half-hourly electricity price forecasting. Therefore the research outcomes demonstrate that the proposed error compensation framework is an effective decision-support tool for improving the predictive accuracy of electricity price. It could be of practical value to energy companies, energy policymakers and national electricity market operators to develop their insight analysis, electricity distribution and market optimization strategies.

A Dataset for Electricity Market Studies on Western and Northeastern Power Grids in the United States

Efficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a comprehensive dataset containing generator aggregations, generator costs, transmission limits, load distributions, and electricity prices for the WECC and NPCC systems based on real-world grid operation data at year 2020, including power plant geographic locations, generation profiles, regional power flow interchanges, and load distributions in both regions. The electricity price from the developed dataset is simulated based on the other items in the dataset, and we show that the variation of the simulated electricity price reasonably aligns with the real-world electricity price in both the WECC and NPCC regions. Overall, the developed dataset is of interest for various electricity market and economic studies, such as the economic dispatch and locational marginal price (LMP) analysis.

Multi-agent based modeling and learning approach for intelligent day-ahead bidding strategy in wholesale electricity market

This work considers a restructured electricity market where the agents are considered as entities, and each agent has a learning capability. Also, the Independent System Operator (ISO) adopted the nodal pricing technique for managing transmission line congestion. In this multi-agent platform, the objective of each Generating Company (GenCo) agent is maximizing its net earnings. GenCo exhibits economic capacity withholding to enhance its daily net income ports updated cost parameters to the ISO. The basic model free policy-based reinforcement algorithms fail to calculate the probability that the rewards obtained are negative or zero. To overcome this drawback, the co-learning agent-based modeling approach implements an interactive learning algorithm applied to GenCo’s for reward consideration in all respects and thereby applies the strategic approach in improvising marginal pricing, commitments, and net incomes. The co-learning effect of GenCo is accomplished with the optimum value of the learning parameters by deploying a novel interactive Variant Roth – Erev (VRE) Reinforcement learning technique for efficient electricity market operation. The introduction of various parameters in learning helps to improve the mutual interaction among the agents and provides better experimentation implementable by GenCo’s meeting the social welfare and smart bidding. The result presented in this paper suggests that the learning capability of the agents is having an impact on the marginal pricing, which supports the operator in managing the congestion in the transmission line. The analysis showcases the effectiveness of the co-learning approach adopted by the agents in the electricity market which is tested on a standard IEEE system and the performance of the technique is validated on a realistic Indian grid network.

A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms

This article presents the formulation of the optimisation of a manufacturing process, through genetic algorithms, managing the generation and demand of energy in a factory at periodic moments of time. The strategy manages to minimise the daily energy cost and maximise the use of installed renewable energy, also taking advantage of potential battery banks. A time series with a 24-hour horizon of energy production from renewable sources and the electricity supply prices provided by the electricity market operator has been considered. Furthermore, in the simulations, scenarios with different battery capacities have been tested, which has allowed a preliminary study to be carried out for the installation of the electrical storage bank. The results presented in this work show that 6% of energy costs can be saved per day, compared to the current management decided by the manufacturing plant operators.

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.

Local Electricity Market operation in presence of residential energy storage in low voltage distribution network: Role of retail market pricing

Local Electricity Market (LEM) appears as a promising consumer-centric market-based approach that extends the self-consumption method, widely implemented in residential households, to collective self-consumption in the local energy communities, enabled through peer-to-peer (P2P) transactions. To facilitate the integration of LEM in the wholesale electricity market (WEM), it is paramount to comprehend the synergy of retail electricity pricing on the LEM operation hosted in the low-voltage distribution network (LVDN). The paper presents a co-simulation framework consisting of a local electricity market model coupled with a three-phase distribution network simulator to perform a holistic case study for a smart energy community in Ireland. The novel contribution of the work is to explore the potential of local electricity trading in the presence of residential energy storage (ES), under different retail pricing schemes existent in Ireland, by evaluating economic benefits to the energy community and network performance of three-phase LVDN. Extensive simulation studies indicate that the presence of residential ES significantly boosts P2P transactions under static time-of-use (SToU) pricing. These P2P transactions are primarily contributed by energy arbitrage (among customers in LEM) in the winter and surplus PV-generated electricity in the summer. On the other hand, the scheduling of ES under SToU pricing deteriorates the network performance of LVDN in winter, showing the highest active power loss and under-voltage scenario among all the cases. Another unique aspect of LVDN is the voltage unbalance studied and found to be highly correlated with ES operation under SToU pricing. Recommendations have been made to the relevant stakeholders and market actors, identifying key aspects necessary to roll out the LEM under retail electricity pricing schemes.

Modeling Multi-horizon Electricity Demand Forecasts in Australia: A Term Structure Approach

The Australian Electricity Market Operator generates one-day ahead electricity demand forecasts for the National Electricity Market in Australia and updates these forecasts over time until the time of dispatch. Despite the fact that these forecasts play a crucial role in the decision-making process of market participants, little attention has been paid to their evaluation and interpretation. Using half-hourly data from 2011 to 2015 for New South Wales and Queensland, it is shown that the official half-hourly demand forecasts do not satisfy the econometric properties required of rational forecasts. Instead there is a relationship between forecasts and forecast horizon similar to a term structure model of interest rates. To study the term structure of demand forecasts, a factor analysis that uses a small set of latent factors to explain the common variation among multiple observables is implemented. A three-factor model is identified with the factors admitting interpretation as the level, slope and curvature of the term structure of forecasts. The validity of the model is reinforced by assessing the economic value of demand forecasts. It is demonstrated that simple adjustments to long-horizon electricity demand forecasts based on the three estimated factors can enhance the informational content of the official forecasts.

An Optimal Scheme for Installation of PMUs and IEDs to Reinforce Electricity Market Immunity Against Data Attacks in Smart Grid

An intruder can disrupt the electricity market operation by injecting malicious data into the sensor measurements to induce profit/loss to a utility/consumer. Robustness in the electricity market operation against false data injection attacks (FDIAs) can be achieved by optimal deployment of phasor measurement units (PMUs) and intelligent electronic devices (IEDs). The nonlinear relationship between sensor measurements, state variables, and nodal electricity price, along with the financial constraints associated with deploying high-cost measurement devices, increases the complexity of solving the FDIA resilient sensor placement task. The solution demands a tradeoff between the financial implications of installing PMUs/IEDs and the nodal price deviation during FDIA. To overcome the above challenges, the task of identifying strategic sensor locations has been formulated as a binary optimization problem. The solution of the problem derived using binary Harris hawks optimization identifies the most vulnerable bus/branch measurements with regard to nodal price deviation due to FDIA. To generalize the applicability of the proposed scheme, the formulation has been carried out while considering the realistic constraints related to PMU channel limitations and the location of the zero injection bus. The effectiveness of the proposed work has been validated for benchmark test systems against different degrees of FDIAs.