Electricity Market Prices Research Articles

Deep Reinforcement Learning-Based Spatiotemporal Decision of Utility-Scale Highway Portable Energy Storage Systems

Mobile charging is an efficient solution to meet peak charging demand on highways. In this paper we propose a deep reinforcement learning (DRL)-based approach to maximize the revenue of a utility-scale highway portable energy storage system (PESS) for on-demand electric vehicle charging. We consider a PESS that consists of an electric truck, battery, and charging stations on highways. Actions include the selection of stations and routes, the selection of charging and discharging, and the corresponding charging/discharging power selection. The first two are discrete, while the last is continuous. To deal with the hybrid action space and time-varying state space of PESS, we design an action space, observation space, and reward function according to its characteristics; in addition, we develop a state-of-the-art DRL model for online decision-making considering the uncertainty in the real-time market price of electricity. Through numerical simulations with real-world electricity price data in California, we demonstrate the effectiveness of the proposed method.

Evaluation and Power Grid Investment Efficiency under High Quality Development

In the context of the reform of transmission and distribution prices, how to improve the investment efficiency of power networks is a noteworthy issue. This article constructs a transmission and distribution price system under the electricity market price system from the perspective of efficiency, and evaluates the effectiveness of each indicator based on the upper limit, benchmark, and profit criteria of transmission and distribution prices. It establishes evaluation methods for investment efficiency of power grids in various regions and provides the optimal solution based on gross profit margin. Through example analysis, the relationship between transmission coefficient, asset return rate, and investment and energy utilization rate was obtained, which is an important basis for making safe and reliable investment decisions. The experiment shows that the accuracy of the three stage DEA method based on the dataset is higher than 89%, which is higher than other algorithms.

A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing with Revenue-Adequacy and FFR constraints

A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing with Revenue-Adequacy and FFR constraints

A dam management problem with energy production as an optimal switching problem

AbstractWe consider an optimal stochastic control problem for a dam. Electrical power production is operating under an uncertain setting for electricity market prices and water level which has to be kept under control. Indeed, the water level inside the basin cannot exceed a certain threshold for safety reasons, and at the same time cannot decrease below another threshold in order to keep power production active. We model this situation as a mixed control problem with regular and switching controls under constraints. We characterize the value function as solution of an HJB equation and provide some numerical approximating methods. We shall illustrate by numerical examples the main achievements of the present approach.

Research on optimal carbon emissions in the production decision of the coal-fired power plant

Purpose The coal-fired power plants have been confronted with new operation challenge since the unified carbon trading market was launched in China. To make the optimal decision for the carbon emissions and power production has already been an important subject for the plants. Most of the previous studies only considered the market prices of electricity and coal to optimize the generation plan. However, with the opening of the carbon trading market, carbon emission has become a restrictive factor for power generation. By introducing the carbon-reduction target in the production decision, this study aims to achieve both the environmental and economic benefits for the coal-fired power plants to positively deal with the operational pressure. Design/methodology/approach A dynamic optimization approach with both long- and short-term decisions was proposed in this study to control the carbon emissions and power production. First, the operation rules of carbon, electricity and coal markets are analyzed, and a two-step decision-making algorithm for annual and weekly production is presented. Second, a production profit model based on engineering constraints is established, and a greedy heuristics algorithm is applied in the Gurobi solver to obtain the amounts of weekly carbon emission, power generation and coal purchasing. Finally, an example analysis is carried out with five generators of a coal-fired power plant for illustration. Findings The results show that the joint information of the multiple markets of carbon, electricity and coal determines the real profitability of power production, which can assist the plants to optimize their production and increase the profits. The case analyses demonstrate that the carbon emission is reduced by 2.89% according to the authors’ method, while the annual profit is improved by 1.55%. Practical implications As an important power producer and high carbon emitter, coal-fired power plants should actively participate in the carbon market. Rather than trade blindly at the end of the agreement period, they should deeply associate the prices of carbon, electricity and coal together and realize optimal management of carbon emission and production decision efficiently. Originality/value This paper offers an effective method for the coal-fired power plant, which is struggling to survive, to manage its carbon emission and power production optimally.

Temperature Control Strategy to Mitigate Electrical Energy Cost for Air Conditioning

The purpose of this paper is to developed an innovative strategy to support small consumer on reducing the electrical energy cost (EEC), define total electrical energy cost (TEEC) and compute the benefit of the pre-cooling model (BPM). The suggested method enables small consumers to manage the peak electricity demand for air conditioning (AC) autonomously and proactively. In this research, a pre-cooling model (PM) has been applied to anticipate an unexpected level of the electricity price. The main contribution of this research is to develop a PM to avoid an extreme electricity price and examines the EEC, TEEC and compute the BPM during hot days, peak season and considering to the probability spike (Pr). The finding results of research illustrated that the EEC for AC can be reduced from IDR 7539 to IDR 5199 and from IDR 13310 to IDR 9394 for hot days and peak season cases, respectively. The TEEC can be examines to be IDR 5200 and IDR 8888 when the Pr may occur during hot days and peak season, respectively. When applied the proposed method that the consumer and aggregator can earn collective benefit, such as: IDR 2340 (31.03 %) and IDR 3916 (29.42 %) if considering to hot days and peak season respectively; as well as IDR 2159 (28.63 %) and IDR 4422 (33.22 %) if considering to Pr that may occur in the midday and peak season, respectively. Consequently, it can be concluded that the suggested model is effective way to keep comfortable of room temperature, controlling electricity peak demand and reduce electricity cost for the AC. This model is evaluated based on the data from the South Sulawesi electricity market price and demand curves provided by the Indonesian State Electricity Company (referred to as PLN), as well as the value of outside temperature (Tout) considering to characteristic of building in the Makassar region.

Specifics of strategic pricing in the current electricity market (energy supply companies)

Subject. This article discusses the principles of functioning of the wholesale and retail electricity markets in Russia. Objectives. The article aims to study the basic principles of pricing, improvement of the applied methods of electricity pricing in the wholesale and retail markets. Methods. For the study, we used the methods of comparative and systems analyses. Results. The article finds that price positions are closely related to the volume of demand, as well as to the effective use of the installed capacity of generating companies. Conclusions. The results of the study can be used in the practical activities of power supply companies.

Financial arbitrage in electricity markets via virtual bidding

The concept of virtual bidding serves as a pure financial tool in electricity markets, aiming at enhancing their performance. Although virtual bidding helps reduce price gaps between different trading floors, there is evidence suggesting that this does not necessarily lead to improved welfare for market consumers. To gain a deeper understanding of virtual bidding and its impact on electricity market prices, this work delves into the strategic behavior of a financial player engaged in an electricity pool-based market. To this end, a bi-level model is formulated. The upper level optimizes the expected revenues of the virtual bidder while the lower-level represents the clearing of the market under network-constrained economic dispatch through a two-stage stochastic programming. The proposed model derives optimal arbitrage strategies in both scenarios of explicit and implicit virtual bidding. It also assesses the performance of virtual bidding under stochastic generation and transmission contingencies, including considerations of up-to-congestion virtual bidding.

Effectiveness of the renewable energy support program implementation (by the example of solar energy)

Objective: to conduct a comprehensive analysis of the dynamics and prospects of the renewable energy support program implementation and to assess its effectiveness.Methods: comparison, synthesis, system approach, generalization, statistical and economic analysis.Results: the authors conducted a comprehensive analysis of system effects from the implementation of renewable energy support programs. The mechanisms of state support of renewable energy generators (further – RES) were studied. These mechanisms are aimed at reducing emissions and costs, creating new jobs, etc., which provides Russia with a unique opportunity to create a full-fledged RES industry. The authors assessed the economic efficiency of renewable energy development in the Wholesale Electricity and Capacity Market (WECM), taking solar energy for analysis. The benefits were analyzed, which are acquired as a result of renewable energy development, including by implementation of the state program of RES projects support and the RES development program for 2013-2024. Economic and non-economic effects arising in the process of RES industry development were analyzed. These are: substitution of fossil fuel consumed in energy production by conventional generation facilities, due to the replacement of conventional generation facilities by RES facilities; reduction of prices in the wholesale electricity market; reduction of greenhouse gas emissions; reduction of costs of measures to reduce the negative impact on the environment and health protection; reduction of compulsory levied payments; creation of new jobs. In conclusion, the prospects of RES development in the state nearest to Russia – the Republic of Belarus – were analyzed, which is important for energy cooperation.Scientific novelty: a comprehensive analysis of system effects from the implementation of the program to support the RES industry in the Russian Federation was carried out. The efficiency of the support program implementation was proved by the example of solar energy. The prospects of the program implementation in the Russian economy were shown.Practical significance: the obtained results and the revealed regularities can be used to make forecasts of renewable energy development taking into account economic and non-economic consequences. They can be used by the authorities to assess the support policy effectiveness of RES generating facilities, as well as to select the most favorable support mechanisms.

Economic Model-Predictive Control of Building Heating Systems Using Backbone Energy System Modelling Framework

Accessing the demand-side management potential of the residential heating sector requires sophisticated control capable of predicting buildings’ response to changes in heating and cooling power, e.g., model-predictive control. However, while studies exploring its impacts both for individual buildings as well as energy markets exist, building-level control in large-scale energy system models has not been properly examined. In this work, we demonstrate the feasibility of the open-source energy system modelling framework Backbone for simplified model-predictive control of buildings, helping address the above-mentioned research gap. Hourly rolling horizon optimisations were performed to minimise the costs of flexible heating and cooling electricity consumption for a modern Finnish detached house and an apartment block with ground-to-water heat pump systems for the years 2015–2022. Compared to a baseline using a constant electricity price signal, optimisation with hourly spot electricity market prices resulted in 3.1–17.5% yearly cost savings depending on the simulated year, agreeing with comparable literature. Furthermore, the length of the optimisation horizon was not found to have a significant impact on the results beyond 36 h. Overall, the simplified model-predictive control was observed to behave rationally, lending credence to the integration of simplified building models within large-scale energy system modelling frameworks.

Energy, economic, and environmental (3E) performance assessment, comparison, and analysis of airport cargo terminal microgrid system under the islanded and grid-connected modes

Microgrids, which can be grid-connected or isolated, may experience dependability issues as a result of intermittent renewable energy. The implementation of a hybrid renewable energy system (HRES) could alleviate this problem but is still a challenging task because of its interference, uncertain, and unpredictable nature. Currently, the absence of comprehensive assessment impedes large-scale applications. In this study, a novel 3E assessment method-based complete quantitative assessment is suggested and inventively integrated with a newly proposed triangle evaluation scheme. Loads of an airport cargo terminal, which are used to test the assessment approach, are classified into critical and non-critical categories according to their necessity. The microgrid system flexibility is considered by the trade-off of non-critical loads. Results show that the assessment method is more intuitive in quantifying the system's performance. The Levelized Cost of Energy (LCOE) in the islanded mode is lower than the current electricity price (from 0.5407 to 0.5439 CNY/kWh) and the annual carbon emission is reduced to one-third of that in the grid-connected mode. Its flexibility can effectively reduce dependence on backup power generation, where the daily peak shedding increases from 217.88 kW to 555.04 kW and daily hours without charging from 6.7 h to 8.9 h. The carbon emission decreases by 32 % considering the impacts of the system flexibility. Besides, the critical distance exists when the LCOE exceeds the current market electricity price.

Modelling the Interdependence of Multiple Electricity Markets in the Distribution System Aggregator Bidding

The volatility of market prices and the interdependence of multiple markets make it challenging for Distribution System Aggregators (DSAs) to model these prices. In this paper, a novel statistical model based on Gaussian process regression and mutual information screening technique is developed. This model is able to predict different market prices and quantify their uncertainty whilst incorporating the interdependence of different markets. The proposed model is employed to assist DSAs with market price modelling. Price scenarios for various markets generated by this model make it viable to formulate the optimal involvement of DSAs in multiple markets as a stochastic multi-step two-stage problem. Other than providing a set of scenarios that efficaciously model multiple electricity market prices, after the clearing of each market, the proposed model leverages market clearing results to improve the accuracy of price prediction of subsequent markets. Extensive simulation results on large price datasets demonstrate that the proposed methodology will result in a considerable increase in the profit of the DSA compared to state-of-the-art price prediction approaches.

Forecasting the price index on the energy market of Ukraine considering the effect of the “green” tariffs laws

The "green" tariff laws in effect in Ukraine were adopted without the necessary technical and economic justification. These laws provide RES owners a wide range of preferences, which in total lead to hypertrophied losses of the Ukrainian energy market. In the process of the state developing a set of measures to radically improve the existing situation, it is necessary to have a wide range of methods of system analysis, including the determination of price indices on the energy market. In the paper a new method and a corresponding mathematical model for determining price indices on energy markets are proposed and tested on real data in conditions of solar and wind power plants functioning in the integrated energy system structure and when "green" tariff laws actions. The model determines the size of the electricity production sector, where competitive relations are maintained. Then the total costs of the energy market are calculated for the summary production of electricity in the power system at both market and fixed prices. The new price is defined as the ratio of total costs to the capacity of electricity production in the sector with competitive relations. The price index is calculated as the ratio of the new price to the market price recorded on the energy market in the base year. Calculations based on real data of the Ukraine’s IES for 2021 showed that if there were no RES in the energy system structure, the market price for electricity would be 2.17 times lower than the actual price. Research of the price index on the energy market of Ukraine at the development of its IES in accordance with the approved government documents showed that at the level of 2030 the average price for electricity will increase by 3.74 times. The country's economy will not be able to withstand such an electricity prices increasing. To solve this problem of national importance, a fundamentally new approach must be implemented. One of the effective solutions is the organization of RES functioning based on the principle of self-sufficiency, when RES ensures frequency and power stability with its own energy. Keywords: market price, price index, energy system, energy market, preference, payback.

Deep learning integration optimization of electric energy load forecasting and market price based on the ANN–LSTM–transformer method

Introduction: Power load forecasting and market price analysis have become crucial in the context of complex power energy systems and volatile market prices. Deep learning technology has gained significant attention in time series forecasting, and this article aims to enhance the accuracy and reliability of power load and market price predictions by integrating and optimizing deep learning models.Methods: We propose a deep learning framework that combines artificial neural networks (ANNs), long short-term memory (LSTM), and transformer models to address key challenges in electricity load forecasting and market price prediction. We leverage ANNs for their versatility and use LSTM networks for sequence modeling to generate initial predictions. Additionally, we introduce transformer technology and utilize its self-attention mechanism to capture long-distance dependencies within the data, further enhancing the model’s performance.Results: In our experiments, we validate the proposed framework using multiple public datasets. We compare our method with traditional forecasting approaches and a single-model approach. The results demonstrate that our approach outperforms other methods in predicting power load and market prices. This increased accuracy and reliability in forecasting can be of significant value to decision-makers in the energy sector.Discussion: The integration of deep learning models, including ANN, LSTM, and transformer, offers a powerful solution for addressing the challenges in power load and market price prediction. The ability to capture long-distance dependencies using the transformer's self-attention mechanism improves forecasting accuracy. This research contributes to the field of energy and finance by providing a more reliable framework for decision-makers to make informed choices in a complex and dynamic environment.

Beyond the merit order effect: Impact of the rapid expansion of renewable energy on electricity market price

A drop in the average electricity market price owing to renewable energy with low marginal costs has been identified and explored as merit order effect. However, diffusion of variable renewable energy leads to concerns about the need for flexibility, decline of supply capacity, and volatility increase of electricity prices. For both decarbonization and stable electricity systems, understanding the drivers of market price is critical, but it is challenging because electricity markets are non-linear and affected by multiple factors. Hence, this research proposes a model based on machine learning techniques and explainable artificial intelligence (XAI) and estimates the multi-directional impact of renewable energy on the Japanese electricity market. The results reveal that the contribution of demand to market price is the largest, followed by solar generation and operable power facility capacity. This research identifies a large decline in the price triggered by solar power during the daytime; however, the effect of solar power varies by the time of day, season, and demand. Additionally, the results suggest that the market price increases when demand is high and solar generation is low, such as during summer evenings. Using XAI, this study quantitatively and visually demonstrated that interactions between solar power, demand, and operable power facility capacity are the key factors behind the high market volatility and price surge. It is important to manage the pace of plant installation and energy transition. Our study provides insights into how and when the market price changes with variable renewable energy and other policy-making factors.

Game-Theoretic Capacity Expansion Analysis Under Carbon Emission Constraints

In this paper, we develop a long-term capacity expansion analysis model that considers strategic bidding behavior by dominant generation companies together with new entry decisions, including renewables, to meet a long-term carbon reduction constraint. We deploy a centralized quadratic programming formulation of the Nash-Cournot game to analyze day-ahead electricity market prices. In our model, strategic producers simultaneously make investment and production decisions, deploying an open-loop Cournot game. We have implemented the model for Ukraine's day-ahead electricity market, which has been highly prone to market power issues. The case study results show significant inefficiencies in peak prices and explore scenarios of alternative mitigation options and their efficacies. Finally, the case study is extended to also explore implications of a low carbon future through emission-constrained runs, evaluation of the performance-based emission policy, and sensitivity on different portfolios of new renewable investors.

Peer-to-peer trading price and strategy optimization considering different electricity market types, tariff systems, and pricing models

This study investigated the peer-to-peer (P2P) trading price and strategy based on market-driven methods within a residential microgrid consists of two prosumers (i.e., P1 and P2) and one consumer (i.e., C1), which establishes a diverse microgrid trading market in terms of peer characteristics (e.g., system configurations and electrical load profile). Five cases are proposed in this study considering different electricity market types (i.e., peer-to-grid and P2P markets), retail tariff systems (i.e., progressive and time-of-use (ToU) tariff systems) as well as market-driven P2P trading pricing models (i.e., uniform and individual pricing models). Peer electrical load data are collected from three typical four-person households in Hong Kong along with their domestic electrical appliance utilization pattern. The results indicate that the individual pricing model has led to the dynamics of P2P electricity trading price than the uniform pricing model, and P2P trading is more economically efficient under the ToU tariff system. The maximum electricity bill saving of the entire microgrid can be achieved by 31.33% in summer and by 43.02% in winter. Besides, it is observed that the installation of battery energy storage system (BESS) has facilitated the self-consumption ratio of the renewable energy system to 91.98% in summer and 100% in winter. This implies that the BESS plays a pivotal role in improving the flexibility in managing the P2P trading strategy, and enhancing the efficiency in electricity dispatching within the microgrid. This study contributes to the novelty in science since it provides a comprehensive framework that can adapt to changing market conditions.

Optimal economic-emission load dispatch in microgrid incorporating renewable energy sources by golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO)

This article proposes the use of hybrid technique to achieve balanced and compromised solution among the generation cost and the emission of pollutants. Microgrids (MGs) are the restricted power energy system that transmitted, generated, and distributed. The renewable energy sources (RESs) are used in their fullest extent. Advantages of MG include reducing cost and transmission losses. Operated in different modes like wind turbine (WT), microturbine (MT) and fuel cell (FC). The proposed technique used to execute the golden jackal optimization (GJO) and Mexican Axolotl optimization (MAO) named as GJO–MAO technique. The objective of the technique is to solve dissimilar optimization issues in MG reduces the computational cost and maximize performance. The objectives of economic dispatch are based on fractional scheduling and restricted environment. Three different scenarios, low-voltage MG system are investigated. GJO–MAO techniques used to optimize various issues on MG by using renewable energy. The proposed technique performance is done in the MATLAB. When the time-of-use (TOU) energy market price strategy with the fixed pricing approach, the economic dispatch is calculated by time-of-use electricity market pricing method, generating cost decreases by 18.5%, 13.5% if the FP-related combined economic emission dispatch (CEED) is examined, and 15% after evaluating the environmental-constrained-economic-dispatch (ECED). The MG producing cost targets for ECED and ECD are according to renewable energy sources. The best and most system is used for finding a fair compromise between the cost of generating and emission. The smallest values of implementation time and standard deviation of superiority and robustness are achieved.

Greed is good? Of equilibrium impacts in environmental regulation

This paper presents a novel method to quantify the market equilibrium impacts of tighter environmental policies. The approach is taken to evaluate the welfare effects of emerging environmental regulations to protect the biodiversity of river ecosystems that put pressure on reducing hydropower generation capacity. A dataset of 129 million bids in the Nordic electricity market is used to calculate how small reductions in hydropower capacity change market outcomes and surpluses hour-by-hour for the years 2011–2022. Reaching the EU Biodiversity Strategy target of 2030 is estimated to reduce 56 MW of hydropower capacity in Finland. Such a change would lead to quantifiable welfare losses of €62 million over time, not counting environmental benefits. Notably, the producer surplus increases by €318 million over time through electricity market price changes, suggesting that instead of lobbying against biodiversity enhancing regulation, agreeing to an industry-wide implementation could benefit both the environment and the firms. Yet the fact that consumer costs increase more than the mere implementation cost reads as a sign of caution when tighter biodiversity policies are introduced.

Unlocking market secrets: Revealing wholesale electricity market price dynamics with a novel application of spectrum analysis

Understanding market participants' competitive behaviour is essential for optimising financial performance in liberalised electricity markets. However, this is challenging due to complex market structures, generation dependent on different primary energy sources and lack of transparency. This paper introduces a novel approach using power spectrum analysis applied to wholesale electricity markets to uncover hidden patterns. Applying this novel method to the Western Australian Wholesale Electricity Market (WEM) revealed periodic cycles in different fuel types and technologies that offered insights into competitor behaviour not immediately evident in the dataset. Surprisingly, the approach uncovered that in a power system with high penetration of renewable generation, there is a weak price response to demand changes, challenging assumptions about the direct link between demand and price formation. These insights could be applied gain a competitive edge in capital investment decisions and tactical bidding behaviour.