Electricity Market Prices Research Articles

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.

Market analysis for the integration of new power technologies: A case study of the deployment of hybrid fossil-based generator plus energy storage (ES-FE)

This study examines the national landscape of hybridized fossil energy (FE) power plants with energy storage (ES) technologies (“ES-FE”) and presents the compilation of an ES-FE dataset, which includes over 65 ES-FE projects and concepts in the United States, comprising approximately 500 MWh of co-located ES capacity with FE power plants. This study also estimates the economic feasibility of adding ES to existing FE power plants by characterizing the potential revenues that can be generated by the ES component through flexibility and capacity value. The analysis focuses on ES technologies with 2- to 10-h. durations located in four U.S. independent system operators (ISOs): Midcontinent ISO (MISO), Electric Reliability Council of Texas (ERCOT), PJM Interconnection (PJM), and California ISO (CAISO), which have +70,000 MW of combined FE power capacity that could add ES. Annual revenues are estimated for the ES component using a what-if-analysis approach, for capacity value, price arbitrage, or ancillary services provision. The results show that annual revenues depend on the end-use storage service, wholesale electricity and capacity market prices, and ES technology operation parameters such as discharging duration and cycling frequency. When performing a sensitivity analysis, ES accrues $7–178/kW-yr. via price arbitrage and ancillary services provision in the four ISOs, and $13–92/kW-yr. when providing capacity value only in MISO and PJM. A cash flow analysis is performed to estimate the net present value (NPV) of the ES addition using a range of ES costs. The study finds that for most ES technologies considered, these revenues alone are insufficient to achieve economic feasibility. Of the 1645 total runs analyzed, 115 had positive NPVs (7%). Therefore, other revenue streams or monetizable benefits are necessary to achieve the break-even point.

Information gap decision theory-based optimization of joint decision making for power producers participating in carbon and electricity markets

Carbon markets have been established in many countries and regions with the goal of promoting global carbon neutrality. The position of power producers as the dominant carbon emitters necessitates that they engage in both electricity and carbon markets. However, most studies have considered only short-term electricity markets and unrealistically static carbon markets, and the speculative behavior of power producers in the carbon market remains poorly considered. The present study addresses these issues by proposing an optimized joint decision model based on information gap decision theory to facilitate the participation of power producers in annual and monthly electricity markets, and monthly carbon markets, where uncertainties in the prices of electricity and carbon quotas, and the speculative behavior of power producers in the carbon market are explicitly considered to ensure that the revenues of market participants do not fall below a predetermined minimum acceptable value. The results of simulations based on the rules and actual market data obtained for electricity and carbon markets in a specific province of China demonstrate that the proposed model provides power producers with trading solutions to meet different expected revenue targets, and thereby assists them as much as possible in counteracting the risks to profit associated with fluctuations in electricity and carbon market prices.

State and federal nuclear support schemes in dynamic electricity market conditions: Insights from NYISO and PJM

Since 2017, several U.S. states have put in place out-of-market financial support schemes for nuclear power plants operating in deregulated electricity markets. In late 2021, the federal government announced the introduction of two new support schemes to secure the continued operation of nuclear power plants. This policy perspective evaluates the profitability of state subsidized nuclear power plants in the NYISO and PJM markets over a five-year period between 2017 and 2021. Results indicate that apart from 2019, nuclear power plants were financially robust, relying solely on market revenues without the need for state support schemes. More importantly, the recent upswing in competitive electricity market prices suggests that additional federal support schemes are not economically justified in current market conditions. I provide several suggestions to reconfigure the support schemes to reflect dynamic market conditions and ensure only vulnerable plants are granted out-of-market support.

Examining the synergistic diffusion process of carbon capture and renewable energy generation technologies under market environment: A multi-agent simulation analysis

To achieve carbon neutrality, the power industry will vigorously promote renewable energy generation and carbon capture and storage (CCS) technologies. The diffusion of these technologies will vary depending on the incentive policies and market environments. Considering the progressive development of China's electricity market reform, this study constructs a multi-agent simulation model for the long-term low-carbon transformation of power systems, simulating the effects of different market environment and policy tools on the synergistic diffusion process of CCS and renewable energy generation technologies. According to the research results, when the price constraints in the electricity market are relaxed, opting for a differentiated clearing price demonstrates greater benefits for the diffusion of CCS technology and the reduction of carbon emissions compared to using a uniform clearing price. In this situation, the spread of renewable energy generation technologies aids in lowering the average market electricity price, thereby offsetting any potential price hikes resulting from the expansion of CCS technology. Moreover, in the presence of electricity price constraints, policy subsidies are necessary to incentivize the diffusion of CCS technology, with the suitable period for such incentives falling before 2040.

A Hyperbolic Bid Stack Approach to Electricity Price Modelling

Modelling the energy price in the Australian National Electricity Market (NEM) requires features that are not well reflected in existing models. We present a semi-structural, multi-regional model wherein bidding is not required to be cost-based, renewable fuels and storage technology are structurally integrated, and network constraints are often binding in optimal dispatch. Available fuel capacity then does not necessarily sum to registered bid capacity, as-bid fuel costs do not dependably follow input fuel prices, and cross-regional interconnectedness requires modelling trade. Furthermore, modelling the NEM spot price path must admit price negativity and price spikes. Extending previous work in the literature, the present paper proposes a hyperbolic bid stack approach to price modelling under these conditions.

Bi-Level Emission Reduction Model of the Hybrid Power Market Based on Carbon Emission Flow Theory and Source–Load Coordination

Limited by the influence of network topology and other factors, the theory of carbon emission flow is unreasonable in the allocation of carbon responsibility on the user side, which leads to the low enthusiasm of users to respond to emission mitigation. The emergence of bilateral transactions provides users with the freedom to choose the type of power supply which is of great significance to exploring the potential of users to reduce emissions and promote the consumption of new energy. For this reason, this paper proposes a bi-level emission reduction model of the hybrid electricity market considering carbon emission flow and source–load coordination. The upper level aims to maximize the revenue of wind, photovoltaic, and thermal power generators and establishes a market-clearing model based on the trading rules of the hybrid electricity market to obtain the bid-winning power of each generator and the hybrid market electricity price. After the market is cleared, the carbon emission liability of the user side is calculated by using the carbon emission flow theory. The lower level takes the minimum cost of electricity consumption as the target and uses electricity price and carbon responsibility as incentives to establish a decision-making model for users to purchase electricity and guide users to actively choose green energy for consumption. The results of the example show that compared with the single pool market trading model the carbon emissions of the system are reduced by 11.9% while the income of the new energy power generations is increased by 9.84% and the electricity cost of the user is reduced by 21.2%, which underlines a mutually beneficial outcome for all stakeholders in the market.

Forecasting the Spot Market Electricity Price with a Long Short-Term Memory Model Architecture in a Disruptive Economic and Geopolitical Context

In this paper, we perform a short-run Electricity Price Forecast (EPF) with a Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM), using an algorithm that selects the variables and optimizes the hyperparameters. The results are compared with one of the standout machine learning algorithms, namely eXtreme Gradient Boosting (XGB). Apart from other EPF solutions, in this paper, we focus on the interval before and after the pandemic and the conflict in Ukraine. Furthermore, compared to the previous papers that mainly approached German, Austrian, Australian, Spanish, Nordic electricity Day Ahead Markets (DAM), we emphasize on the EPF for one of the East-European countries—Romania whose market rules closely align with the rules of the European Union electricity DAM. The contribution of this study consists in creating a data set that spans from January 2019 to August 2022 and providing an algorithm to identify the best stacked LSTM architecture to cope with a challenging short-term EPF. The proposed algorithm identifies the most relevant variables using a correlation threshold and performs a combination of three parameters—hidden layer size, dropout and learning rate generating the best EPF results.

Dynamic scheduling of market price-based combined heat–power-constrained renewable microgrid

Abstract In this paper, a market price-based combined heat–power dynamic dispatch model for a microgrid is presented. The microgrid comprises cogeneration units and wind and solar power-generation units. A battery and a heat storage tank are incorporated to optimally balance variations in heat-and-power load demands. The proposed model explores the impact of market prices of electricity, heat supply and load variability on the optimal schedule such that profit maximizes and emission, loss and waste heat are minimized. The Weibull probability distribution function is applied to characterize the uncertain renewable power variable in the model and to find the over- and under-scheduling costs. The problem is solved using an improved differential evolution algorithm in which a fuzzy membership module is appended to obtain a solution having the highest attainment for the selected multiple objectives. The results show that the proposed model can handle uncertain heat–power demand and price scenarios to produce feasible and optimal schedules with owner profits, heat utilization and renewable share varying between 10.55–115.97%, 72.51–90.39% and 26.82–38.05%, respectively.

Testing the Factors Affecting Intraday Market Electricity Prices by Connectedness Approach

This study aims to dynamically analyse the relationship between intraday market electricity prices and day-ahead market electricity prices and the amount of electricity generated based on the primary energy resource in Turkey. In this context, the data set consisting of electricity prices in the day-ahead market, electricity prices in the day-ahead market, and electricity generation amount based on primary energy resources, covering the period from 1 January 2018 to 19 June 2022, was analysed with TVP-VAR. Findings reveal that the relationship between variables changes over time and is affected by global events. Furthermore, it has been determined that the intraday market has moved from a general receiver of volatility to a general transmitter in the post-Covid 19 period.

The Influence of Photovoltaic Self-Consumption on Water Treatment Energy Costs: The Case of the Region of Valencia

Energy consumption is one of the principal components of the operative costs incurred by providers of water services, both financial and environmental. Fortunately, in recent years solar panel technology has improved substantially, and photovoltaic self-consumption has become a tool that can reduce the costs of water reuse and other water services. Regions with a scarcity of water resources make a considerable use of non-conventional sources, consuming a significant amount of energy, which has a high financial and environmental cost and compromises the sustainability of the water supply. This research analyses the possibility of replacing part of this energy with self-consumption through photovoltaic panels based on data obtained for the Region of Valencia in order to analyse the impact of energy substitution on energy costs. Performing a Cost–Benefit Analysis, self-consumption projects require an electricity market price of between 0.14 and 0.18 EUR/kWh, so in financial terms it is not a particularly attractive alternative. However, the avoided greenhouse gas emissions have a high value, and including them in the calculations, the price needed to be in feasible amounts of 0.04–0.10 EUR/kWh for a small installation and 0.02–0.08 EUR/kWh for a large one. In other words, photovoltaic self-consumption is still today an alternative with financial difficulties, but the associated environmental benefit justifies public intervention as it is a beneficial energy alternative in a context of high greenhouse gas emissions.

A Coevolution Algorithm Based on Spatial Division and Hybrid Matching Strategy

With the rapid development of social economy, people's demand for diversified and precise goals is increasingly prominent. In the face of a specific engineering application practice, how to find a satisfactory equilibrium solution among multiple objectives has been the focus of researchers at home and abroad. Aiming at the convergence and diversity imbalance in the current high-dimensional multi-objective evolutionary algorithm based on reference points, this article suggests a constrained evolutionary algorithm based on spatial division, angle culling, and hybrid matching selection strategy. Experimental practices show that the proposed algorithm has better performance compared with other related variants on DTLZ/WFG benchmark functions and in solving the problem of electricity market price.

Regional electricity market price forecasting based on an adaptive spatial–temporal convolutional network

The accurate prediction of electricity prices has great significance for the power system and the electricity market, regional electricity prices are difficult to predict due to congestion issues in regional transmission lines. A regional electricity price prediction framework is proposed based on an adaptive spatial–temporal convolutional network. The proposed framework is expected to better explore regional electricity prices’ spatial–temporal dynamic characteristics in the electricity spot market and improve the predictive accuracy of regional electricity prices. First, different areas of the electricity market are regarded as nodes. Then, each area’s historical electricity price data are used as the corresponding node’s characteristic information and constructed into a graph. Finally, a graph containing the spatial–temporal information on electricity prices is input to the adaptive spatial–temporal prediction framework to predict the regional electricity price. Operational data from the Australian electricity market are adopted, and the prediction results from the proposed adaptive spatial–temporal prediction framework are compared with those of existing methods. The numerical example results show that the predictive accuracy of the proposed framework is better than the existing baseline and similar methods. In the twelve-step forecast example in this paper, considering the spatial dependence of the spot electricity price can improve the forecast accuracy by at least 10.3% and up to 19.8%.

Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm

Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models.

Optimal bidding strategy of multi-carrier systems in electricity markets using information gap decision theory

This paper proposes a novel optimization framework based on information gap decision theory (IGDT) for strategic participation of multi-carrier systems (MCS) in the electricity market under price uncertainty. The proposed framework helps the operator of MCS to take advantage of flexible generation units such as combined heat and power (CHP) systems, boilers, and battery energy storage systems (BESS) and participate in the electricity market considering the strategies presented by IGDT against the uncertainty in the market clearing price. The proposed optimization framework is developed as a bilevel problem, in which the risk-based bidding strategy of the MCS operator is modeled in the upper-level problem, and the energy market is cleared by the bulk power system operator in the lower-level problem. Karush–Kuhn–Tucker conditions are used to transform the bilevel problem into a single-level optimization problem. Duality theory is later used to approximate the product of power and market clearing price in the MCS operation problem, and the non-linearity enforced by the implementation of IGDT is handled by a linearization method called Triangle method. The studied optimization framework is modeled as a single-level mixed-integer linear programming problem, and CPLEX is used to solve it. The simulations are carried out on the modified IEEE 6-bus transmission network, and the results are analyzed to verify the economic and computation efficiency of the studied model. According to the simulation results, by taking a risk-averse bidding strategy provided by the IGDT, the operation cost of MCS increases 16.6% in order to increase the robustness level of MCS up to 36%, indicating that MCS becomes robust against 36% of the increase in the electricity market price, compared to the case with a risk-neutral bidding strategy being taken.

Effects of heat pump scheduling on low‐voltage grids using a receding horizon control strategy

AbstractHeat pumps (HPs) are a promising option for decarbonizing the residential heating sector. Their use leads to an increased demand for electricity, which can burden the grid in uncontrolled operation, while controlled HPs can provide additional flexibility to the system. In this paper, the integration of HPs through a receding horizon approach into a power market model is presented to study the behavior of HPs considering electricity market prices and network constraints of a low‐voltage grid. In a case study with centralized and self‐consumption optimized control strategies, the effects of HPs are analyzed. Furthermore, the authors investigate how the aggregation of several households into energy communities affects the system. The results show that for the considered grid, the additional power demand of HPs is usually uncritical and a strictly cost‐minimizing operation is often possible from a grid perspective. Typically, critical grid situations occur only at times of high PV feed‐in with low HP flexibility. The formation of energy communities compared to individual households differs primarily in the allocation of cost savings to individual consumers. The difference in the impact of the two control strategies on the grid is negligible in this case.

Stochastic trading of storage systems in short term electricity markets considering intraday demand response market

In recent years, the renewable energy sources are used in the power system for reducing environmental pollution. One of these renewable energy sources is wind farm that is used widely. However, they increase the uncertainty of the power system. To solve this problem, plenty researches have proposed several methods that have some disadvantages. This paper tries to decline the effects of uncertainty of wind turbines by using of the energy storage systems and optimal demand response programs. A stochastic planning of energy storage systems and wind generation are presented in the short term electricity markets considering the trading day-head and intraday market of demand response. In order to formulate an accurate model of various components of system, all needed constraints are considered. In this problem, wind turbines, energy storage systems, and demand response aggregator work coordinately that is called virtual power plant. For better comparison, two states consist of coordinated and uncoordinated working of all components are considered and assessed. The various scenarios for electricity market price, adjustment market price, imbalance energy price, and the generated power of wind turbine are considered. These scenarios are generated with hybrid neural network and coati optimization algorithm. Also, the iterative fast progressive Kantorovich method is used for scenario reduction. The results show the better response of the proposed method in optimal operation of virtual power plant.

Assessing the cost competitiveness of electrolytic hydrogen production from small modular nuclear reactor-based power plants: A price-following perspective

Small modular nuclear reactor power plants (SMNRPPs) for hydrogen cogeneration are in early development, and their cost competitiveness for low-temperature electrolysis has not been well-studied. This study combines a quadratically-constrained economic dispatch model and a levelized cost analysis model to evaluate hydrogen cogeneration from a 300 MW SMNRPP coupled with proton exchange membrane electrolyzers. The SMNRPP adjusts its power output based on fluctuating electricity market prices and a time-invariant conversion value for electricity pricing arbitrage. We evaluated the SMNRPP-electrolysis system for 20 years in two case study locations and found the hydrogen supply cost ranged from CAD$1.77–3.36/kg and CAD$2.11–2.77/kg in electricity markets with high and low fossil fuel-dependence, respectively. The SMNRPP-electrolysis system is more cost-effective than fossil fuel-based hydrogen production at CAD$1.54/kg if the SMNRPP capital cost decreases to CAD$4,700/kW. This is because the SMNRPP capital cost accounts for 75% of the hydrogen supply cost variance, while other techno-economic parameters account for less than 27% of the supply cost variance. The system can achieve the lowest cost hydrogen production in under 17 years if the global learning rate and installation growth rates for SMNRPPs exceed 10% and 15%, respectively, to reach a 4.96 GW global footprint. The study outcomes can help formulate policies for SMNRPPs adoption in the hydrogen economy.

A Neural Network Based Approach for the Short-term Forecasting of Electricity Market Price

In this study, an improved cascaded neural network is utilized to determine a Marketing Clearing Price (MCP) for energy in the wholesale market in Russian. Research on MCP prediction has attracted a lot of attention in recent years. This study recommends a unique approach based on a customized back propagation algorithm for retraining, testing, and testing on pricing over many months. This research contributed to the development of the methodology. MATLAB R 2015a (8.1.0.602) based software and simulation will be used to carry out the suggested action. As part of the strategy framework, we provide an improved approach that can reduce the load time while also reducing the MAPE, MSE, and RMSE. The proposed method's end goal is to forecast the energy market clearing price. The MAPE of our proposed technique is the smallest of all that have been reported in the literature. The calculated MAPE for the proposed strategy is 1.9%. The proposed method may be used in a number of different types of electric boards to provide clean, stable electricity