Electricity Market Prices Research Articles

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Integrating hydrogen technology into active distribution networks: The case of private hydrogen refueling stations

Private hydrogen refueling stations (HRSs) are expected to be an integrated part of active distribution networks (ADNs) in the near future. In this paper, we consider an ADN operator, responsible for serving the network’s electricity demands in a cost-effective manner, while ensuring the network’s operational safety. The ADN’s resources include micro-turbines, energy storage systems, and private HRSs that deliberate over buying hydrogen directly, or converting electricity to hydrogen on-site by using their electrolyzers and hydrogen tanks. Each HRS is after maximizing its profit, stemming from serving the stochastic demands of hydrogen vehicles. In the presence of stochastic hydrogen demands, volatile wholesale electricity market prices, and deliberate, profit-maximizing HRSs, the ADN’s goal takes the form of a stochastic-robust bi-level optimization problem. After a number of reformulations, we bring the problem to a solvable form. Numerical results demonstrate the effectiveness of the model towards integrating private HRSs into ADNs, and maintaining the ADN’s safe operation under severe uncertainties.

Aggregate Impact Analysis of Demand Response Programs, Electric Vehicles, and Combined Heat and Power Units on Integrated Management of Industrial Virtual Power Plant

Efficient utilization of existing energy resources within smart industrial grids constitutes an indispensable part of any energy management system (EMS). In this regard, virtual power plants (VPPs) play a crucial role in smart exploitation of available generated power. The present paper offers a new and exciting perspective at the EMS of Industrial VPPs (IVPPs). The grid management simultaneously makes use of demand response (DR) loads and electric vehicles (EVs) deployed at parking spaces. The EMS follows the objective of maximizing overall profit of its assemblage. At the same time, the EMS endeavors to augment the grid reliability at peak conditions and reduce load shedding of industrial centers. Naturally, there are uncertainties in such parameters as electricity market prices, EVs, and renewable energy production sources. Thus, a random-based energy management problem approach is adopted. To validate, the proposed method is experimented on the second zone of the modified IEEE-RTS standard network. The simulations reveal that the EVs’ presence in dedicated parking spaces can bring about a dramatic increase in the storage capacity of the IVPPs. This causes a reduction in the use of network’s overall power capacity. In fact, a prominent feature of the EMS is constant use of DR programs and selecting best one for each IVPP at different time hours. On the whole, the adopted procedure gives rise to a general reduction in network’s operational costs, considerable de-peaking as well as enhanced performance of EVs at parking spaces.

A Predictive Fuzzy Logic Model for Forecasting Electricity Day-Ahead Market Prices for Scheduling Industrial Applications

Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price.

A Compound Up-and-In Call like Option for Wind Projects Pricing

Wind energy projects represent, currently, a valid opportunity to support United Nations Sustainable Development Goal 7. However, these projects can appear financially unattractive considering the unfavorable meteorological conditions, uncertain electricity market price, uncertain market demand, unpredictable project performance, riskiness of investment stages, etc. This paper provides a real options pricing model applied for the evaluation of a wind farm project to include the uncertainty that can affect future performance. The methodology proposed uses a compound call option model with two barriers applied, respectively, to the twofold phase framework that would act as a sort of up-and-in barrier. The compound call option model allows us to valuate the managerial flexibility to proceed with the following investment stages depending on the success of the previous ones and, through the barriers, the methodology gives the investor the opportunity to consider some profitability thresholds below, past which the investment should be abandoned. We develop a discrete case methodology by using the binomial approach. A hypothetical case study is shown to implement the theoretical framework by using likely data.

Economic feasibility of floating photovoltaic power plants: Profitability and competitiveness

Floating photovoltaics is a solution that is getting significant attention worldwide. It consists of installing photovoltaic modules on water basins. This contributes to alleviating the land occupancy issue associated with the growing deployment of photovoltaics. However, because of the early stage of deployment, the costs of floating systems are still higher than traditional land-based photovoltaics. Nonetheless, in some cases, the installation of floating photovoltaics might occur independently of the cost competition with the in-land technology. In this light, the current work presents an analysis of the profitability and cost competitiveness of floating photovoltaics in Europe. The Levelized Cost of Electricity of floating photovoltaics is found to be generally higher than onshore wind and land-based photovoltaics, but lower than fossil gas. Furthermore, floating photovoltaics is found to be a profitable investment in countries with low capital costs, high energy yields and/or high market price of electricity. In these countries, the internal rate of return is typically higher than the weighted average cost of capital, confirming the profitability of the investigated solution. Last, the economic parameters are found to influence the economic viability of FPV more than the energy related factors such as the tilt angle or the heat transfer.

Day-ahead to intraday energy scheduling operation considering extreme events using risk-based approaches

Demand response programs, energy storage systems, electric vehicles, and local electricity markets are appropriate solutions to offset the uncertainty associated with the high penetration of distributed energy resources. It aims to enhance efficiency by adding such technologies to the energy resource management problem while also addressing current concerns using smart grid technologies and optimization methodologies. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers the uncertainty associated with load consumption, renewable generation, electric vehicles, electricity market prices, and the existence of extreme events in a 13-bus distribution network with high integration of renewables and electric vehicles. A risk analysis is implemented through conditional value-at-risk to address these extreme events. In the intraday model, we assume that an extreme event will occur to analyze the outcome of the developed solution. We analyze the solution’s impact departing from the day-ahead, considering different risk aversion levels. Multiple metaheuristics optimize the day-ahead problem, and the best-performing algorithm is used for the intraday problem. Results show that HyDE gives the best day-ahead solution compared to the other algorithms, achieving a reduction of around 37% in the cost of the worst scenarios. For the intraday model, considering risk aversion also reduces the impact of the extreme scenarios.

A flexible techno-economic analysis tool for regional hydrogen hubs – A case study for Ireland

The increasing urgency with which climate change must be addressed has led to an unprecedented level of interest in hydrogen as a clean energy carrier. Much of the analysis of hydrogen until this point has focused predominantly on hydrogen production. This paper aims to address this by developing a flexible techno-economic analysis (TEA) tool that can be used to evaluate the potential of future scenarios where hydrogen is produced, stored, and distributed within a region. The tool takes a full year of hourly data for renewables availability and dispatch down (the sum of curtailment and constraint), wholesale electricity market prices, and hydrogen demand, as well as other user-defined inputs, and sizes electrolyser capacity in order to minimise cost. The model is applied to a number of case studies on the island of Ireland, which includes Ireland and Northern Ireland. For the scenarios analysed, the overall LCOH ranges from €2.75–3.95/kgH2. Higher costs for scenarios without access to geological storage indicate the importance of cost-effective storage to allow flexible hydrogen production to reduce electricity costs whilst consistently meeting a set demand.

Fuzzy Q-learning-based approach for real-time energy management of home microgrids using cooperative multi-agent system

The development of plug-in electrical vehicles (PEVs) and distributed energy resources in home microgrids (H-MGs) is gaining attention in recent years. Accordingly, it is vital to apply a suitable energy management system to provide the required energy of the PEV and ensure the health of the energy storage systems in the H-MG. This paper proposes a continuous real-time cooperative multi-agent system (MAS) for H-MG. Real-time MAS interacts with H-MG agents to perform as independent learners applying a distributed and cooperative reinforcement learning method, while state variables are shared to coordinate their real-time behavior. Therefore, the fuzzy Q-learning (FQL) method is investigated to control the agents in the continuous state space and achieve an efficient solution. The experimental results highlight the effectiveness of the suggested control strategy to guarantee the energy supply and reduce the electricity market price. The results of the simulation demonstrate that using the proposed cooperative MAS with the real-time FQL method in the energy management of the H-MG will reduce the electricity market price by 10%, increase the health of the storage system by 35.67%, and enhance the utilization of the PV system to charge PEV for 8% at the peak load demand.

Local electricity market pricing mechanisms’ impact on welfare distribution, privacy and transparency

Local energy communities and electricity markets have emerged as possibilities for interaction among prosumers. A substantial effort has been invested into creating efficient pricing mechanisms for various market arrangements, all of which take into consideration distinct characteristics of local electricity trading. However, since they are all evaluated in terms of various systems and market conditions, it is challenging to directly compare the mechanisms. In this research, three well-established pricing mechanisms from the literature are systematically compared and evaluated under identical settings on their influence on welfare distribution across various market participant groups, privacy protection, transparency and complexity level. According to the findings, the supply–demand ratio pricing system leads to the lowest costs for consumers and is also the most privacy compliant and transparent. Furthermore, prosumers obtain the highest cost-savings through the consensus alternating direction method of multipliers pricing mechanism, whereas the equilibrium pricing mechanism performs best regarding economic fairness. The aim of this article is to provide insight into the performance of different pricing mechanisms to energy regulators and local electricity market facilitators. The comparative analysis should aid in making informed decisions on the implementation of local electricity markets.

A New Design for the Peer-to-Peer Electricity and Gas Markets Based on Robust Probabilistic Programming

This paper presents a fully-decentralized peer-to-peer (P2P) electricity and gas market for retailers and prosumers with coupled energy units, considering the uncertainties of wholesale electricity market price and prosumers’ demand. The goal is to improve the overall economy of the proposed market while increasing its flexibility. In this market, the retailers are equipped with self-generation and energy storage units and can bilaterally negotiate for electricity and gas transactions with prosumers to maximize their profit. Furthermore, they can sell power to the upstream market in addition to prosumers. The prosumers have access to several retailers to supply their required electricity and gas and can freely provide their energy needs from every retailer, contributing to dynamicity in the proposed market. Given that they have an energy hub consisting of boiler units, combined heat and electricity (CHP) units, and electric pumps, they can switch their energy supply source from electricity to gas and vice versa. A robust possibilistic programming approach is applied to address the uncertainties. A fully-decentralized approach called the alternating direction method of multipliers (ADMM) is utilized to solve the presented decentralized robust problem. The proposed decentralized algorithm finds an optimum solution by establishing a smart balance between the average expected value, optimality robustness, and feasibility robustness. The feasibility and competitiveness of the proposed approach are evaluated through numerical studies on a distribution system with two retailers and three prosumers. The data analysis of the simulation results verifies the effectiveness of the proposed decentralized robust framework as well as the proposed decentralized solution. According to the maximum deviation, the expected optimal value in the robust case, the retailer’s profit has decreased by 12.1 percent, and the prosumers’ cost has increased by 27.4 percent due to the feasibility penalty term.

Providing a mathematical model to select governmental supporting scenarios for renewable energy plants

ABSTRACT In this study an extended model is proposed based on government subsidies to support the development of new energy power plants. Accordingly, the profit of this production unit is increased with the reduction of CO2 emissions. In addition, the results show that the plan to sell carbon dioxide emissions threshold helps in financial subsidies saving. Also, the increase in factors such as energy production capacity, electricity market price, CO2 emission threshold price and fluctuations in investment costs can lessen the amount of dedicated subsidies by the government. Also, the increase in parameters such as investment costs and fluctuations in electricity prices, and the price of CO2 emission thresholds leads to the dedication of more encouragement subsides.

Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation

This paper aims to improve the forecasting of electricity market prices by incorporating the characteristics of electricity market prices that are discretely affected by the fuel cost per unit, the unit generation cost of the large-scale generators, and the demand. In this paper, two new techniques are introduced. The first technique applies feature generation to the label and forecasts the transformed new variables, which are then post-processed by inverse transformation, considering the characteristic of the fuel types of marginal generators or prices through two variables: fuel cost per unit by the representative fuel type and argument of the maximum of Probability Density Function (PDF) calculated by Kernel Density Estimation (KDE) from the previous price. The second technique applies decomposition to the demand, followed by a feature selection process to apply the major decomposed feature. It is verified using gain or SHapley Additive exPlanations (SHAP) value in the feature selection process. In the case study, both showed improvement in all indicators. In the Korean Electricity Market, the unit generation cost for each generator is calculated monthly, resulting in a step-wise change in the electricity market price depending on the monthly fuel cost. Feature generation using the fuel cost per unit improved the forecasting by eliminating monthly volatility caused by the fuel costs and reducing the error that occurs at the beginning of the month. It improved the Mean Squared Percentage Error (MAPE) of 3.83[%]. Using the argument of the maximum PDF calculated by KDE improved the forecasting during the test period, where discrete monthly variations were not included. The resulting MAPE was 3.82[%]. Combining these two techniques resulted in the most accurate performance compared to the other techniques used, which had a MAPE of 3.49[%]. The MAPE of the forecasting with the decomposed data of the original price was 4.41[%].

Energy storage system impact on the operation of a demand response aggregator

In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.

A graph-based superframework for mixture model estimation using EM: an analysis of US wholesale electricity markets

A fully unsupervised graph-based superframework is proposed to handle the EM initialization problem for estimating mixture models on financial time series. Using a complex network approach that links time series and graphs, the graph-structured information derived from the observed data is exploited to produce a meaningful starting point for the EM algorithm. It is shown that structural information derived by complex graphs can definitely capture time series behavior and nonlinear relationships between different observations. The proposed methodology is employed to estimate Gaussian mixture models on US wholesale electricity market prices using two different configurations of the superframework. The obtained results show that the proposed methodology performs better than conventional initialization methods, such as K-means based techniques. The improvements are significant on the overall representation of the empirical distribution of log-returns and, in particular, on the first four moments. Moreover, this approach has a high degree of generalization and flexibility, exploiting graph manipulation and employing functional operating blocks, which can be adapted to very different empirical situations.

Tool for optimization of sale and storage of energy in wind farms

In this work we address the problem of energy management in a wind farm supported by an Energy Storage System (ESS) that operates in an electricity market with six intraday sessions and with penalty policies for imbalances between commitments and the energy really injected. We face it through a cascade of model predictive controllers that also require the design of predictors for wind and electricity market price forecasts. The master controller is executed synchronously with the market sessions and decides the commitments. The slave controller is executed each hour and decides the energy that should be sold to minimize the economical penalties if the commitment is not achievable. Finally, a real-time controller decides how to manage the energy storage in the ESS to sell the desired energy when possible. We use historical real data for the design and validation of the approach and show its benefits. The results show that the cascade structure helps to adequately adapt the energy committed in the intraday market. We also obtain the necessary prices on batteries so that their use is profitable.

Hybrid robust-stochastic multi-objective optimization of combined cooling, heating, hydrogen and power-based microgrids

This paper presents a holistic structure to reach the optimal operation of a combined cooling, heating, hydrogen, and power (CCHHP)-based microgrid (MG). The CCHHP-based MG under study includes a wide variety of energy conversion facilities like power-to-hydrogen (P2HY), hydrogen-to-power (HY2P), electrical chiller (EC), absorption chiller (AC), heat pump (HP), gas boiler (GB), combined heat and power (CHP), wind turbine, and energy storage systems. In the presented structure, a robust-stochastic coordinated optimization method is developed to minimize both daily operation cost and carbon pollution emissions with particular attention to uncertain sources, i.e., electricity market price, renewable power production, and energy demands. Furthermore, the incentive-based demand response (DR) program is used to increase the flexibility of the CCHHP-based MG and manage the behavior of the electrical and heating demands to use the economic opportunities in energy markets. The proposed multi-objective optimization structure is modeled as a mixed-integer linear programming (MILP) for optimal energy management using the ε-constraint method. The developed structure is solved using a CPLEX solver under the GAMS software environment. Several case studies and numerical results are provided to confirm the effectiveness of the proposed structure. Obtained simulation results indicate that utilizing the up-to-date energy conversion facilities, i.e., P2HY, HY2P, and EC, in the optimal coordinated operation with the incentive-based DR program can reduce the system operation cost to 15.44%.

Battery-hydrogen vs. flywheel-battery hybrid storage systems for renewable energy integration in mini-grid: A techno-economic comparison

Decentralized renewables power production is rapidly growing because of environmental concerns. With the purpose of maximizing renewable exploitation, energy storage systems integration in Mini-Grids (MGs) is needed. Hybrid energy storage systems (HESSs) are promising to obtain enhanced performances in terms of both capacity and responsiveness, yet their feasibility may be hindered by design and management choices impacting the economic competitiveness. In this paper, two HESSs are analysed and compared in a real case-study, namely reversible solid oxide cell (rSOC)/Li-ion battery and flywheel/Li-ion battery systems. The analysis here developed handles annual photovoltaic generation and electric load actual dataset and includes: i) a statistical analysis to obtain the most representative days, ii) dynamic modeling of the HESSs, iii) simulations to define components sizing and to extrapolate the evolution of annual battery state-of-charge, iv) battery lifespan assessment through application of the rainflow cycle counting algorithm, and v) a detailed economic evaluation (Levelized Cost of energy LCOE, Levelized cost of Storage LCOS and their sensitivity to electricity market price) including subsidies for self-consumption. For both HESSs, performance indexes of reduction of energy withdrawal from the grid and self-consumption increase are fixed at about 30 % and 70 % respectively, with reference to the features of the investigated MG. As main outcomes for this integrated methodology, it appears that self-consumption rewards (i.e.110 €/MWh) let the flywheel/Li-ion battery HESS reach a LCOS agreeing with 2021 average market parity. On the contrary, LCOS is more than two-fold for the rSOC/Li-ion battery HESS, being equal the impact on the MG management and its energy independence from the main power grid. The integrated methodology is adequate for critically reviewing design and management choices and understanding the impact of the market on the profitability of a sub-system, with insight on energy storage units.

Integrated Power and Economic Analysis of Austria’s Renewable Electricity Transformation

Austria has set the goal to transform its electricity sector to 100% renewable energy sources by 2030. The transition to highly renewable power systems is not only a technical challenge but also has economic implications due to high investment needs. Furthermore, electricity price and demand are interlaced and influence each other, which requires both technical and economic analyses. In order to provide these comprehensive integrated analyses, we present a novel approach of linking the technical model of the continental European electricity system ATLANTIS with the macroeconomic model DYNK. This allows us, inter alia, to analyze the effects of increasing shares of renewables on wholesale electricity prices and demand, and to perform a sensitivity analysis with respect to CO2 prices. Our results show that increasing CO2 prices greatly affect coal-fired generation abroad, which in turn promotes the role of gas-fired generation, at least until 2030. For Austria, this results in increased national gas-fired generation and electricity exports. Therefore, gas-fired power plants still determine the Austrian market price for electricity in the merit order, which leads to higher electricity prices due to CO2 pricing. In turn, however, higher electricity prices only cause a marginal reduction in the electricity demand in Austria.

A coordinated model for self‐scheduling and market participation of a large consumer in the face of a comprehensive set of uncertainties with incomplete Data

AbstractIn a restructured electricity market, a large consumer could supply its electricity consumption through its own available resources or purchasing from the electricity markets. Moreover, it could participate in demand response programs (DRPs) and also supply some portion of its demand from bilateral contracts. In this regard, the large consumer's objective is to minimize its own operation cost while facing several uncertainties that could affect its optimal decisions. The decision‐making problem becomes more difficult especially when the historical data is incomplete, or when the data is not available. This paper proposes an optimal decision‐making framework for a large consumer in the face of a comprehensive set of uncertain resources including electricity market price, internal electricity consumption, renewable power generation, and DR participation. To this end, a probabilistic‐possibilistic methodology is proposed to model the uncertainty of the electricity market prices in a more accurate way in comparison with existing approaches through the concept of Z‐number method. The uncertainty of internal electricity consumption is modelled by means of the seasonal ARIMA model, while the stochastic generation of its existing renewable resources are modelled through appropriate probability distribution functions. Moreover, the uncertainty of DRPs is considered by Markov chain. The large consumer also could sign bilateral contracts with generation companies to provide a portion of its required electricity demand. The simulation results confirm the effectiveness of the Z‐number methodology in comparison with other techniques for handling the uncertainties.