Imbalance Prices Research Articles

Risk-aware participation in day-ahead and real-time balancing markets for energy storage systems

The increasing volatility of electricity prices due to increased uncertain renewable energy generation gives rise to interesting short-term arbitrage opportunities for Energy Storage Systems (ESS) operators. Whereas prior research has shown the possibility to exploit inter-temporal arbitrage opportunities in the real-time balancing market, this paper formulates a two-stage optimization methodology that allows ESS operators to also engage in inter-market arbitrage by participating in both the day-ahead and real-time balancing markets. However, the effectiveness of such a strategy is heavily influenced by expected inter-market price differentials, which is known to be difficult to predict when it involves the imbalance price. To address this issue, we propose a risk-averse approach by incorporating the conditional value at risk in the ESS objective function. The proposed methodology is applied to a case study of the Belgian electricity market, where we demonstrate the effectiveness of (i) the combined market participation compared to an ESS participating in either market, and (ii) the risk-aware methodology by showcasing improved ex-post out-of-sample profit performance of a risk-averse compared to a risk-neutral ESS.

Forecasting Imbalance Price Densities With Statistical Methods and Neural Networks

Forecasting Imbalance Price Densities With Statistical Methods and Neural Networks

Analysis of Imbalance Price Fluctuating Factors Considering Price Structural Change

Analysis of Imbalance Price Fluctuating Factors Considering Price Structural Change

Strategic Implicit Balancing With Energy Storage Systems via Stochastic Model Predictive Control

Battery Energy Storage Systems (BESS) may exploit the increasing price volatility in imbalance settlement mechanisms via inter-temporal arbitrage. However, participating in these markets requires a careful trade-off between expected profits, accounting for the impact of BESS actions on prevailing imbalance prices, the financial risks and the incurred battery degradation costs. This paper introduces a novel forecast-informed Model Predictive Control (MPC) methodology in which a strategic and potentially risk-averse BESS performs implicit balancing by taking out-of-balance positions in near-real time. Thereby it anticipates expected imbalance prices in a European-style balancing market, and takes into account state of charge-dependent battery degradation costs. To this end, an attention-based recurrent neural network forecasting technique is leveraged to predict the System Imbalance. The proposed methodology is tested on a real-life case study of the Belgian balancing market. Expected profits of a 2 MW/2 MWh BESS (21,784 MW/month) are shown to exceed those of different benchmarks available in the literature, including the profit associated with participating in the day-ahead energy market with perfect price foresight (7,082 /MW/month). From a system perspective, these implicit balancing actions performed by the BESS owner reduce the system imbalance in 75% of all cases, thus improving the cost-efficiency of power systems.

Profit enhancement and grid frequency control by energy level scheduling of CAES system in wind-connected electrical system

The maintenance of power balance poses significant challenges in renewable combined deregulated power systems due to the unpredictable nature of renewable energy sources. This situation leads to economic instability within the system. However, an energy storage system can help maintain energy supply and control system stability for renewable incorporated thermal power plants. Unlike in regulated markets, energy prices in deregulated markets are not fixed by any government body or particular company. Instead, the Independent System Operator (ISO) serves as the main entity in the electrical market, gathering tenders from Generation Companies (GENCOs), Distribution Companies (DISCOs), and Transmission Companies (TRANSCOs). The market controller regulates energy prices using Nodal Pricing (NP), which provides economic benefits to both GENCOs and DISCOs. However, the unpredictability of renewable sources often results in a decline in system profit due to the production of an imbalance price (CostIMC) caused by a mismatch in contracted power generation from the renewable power plant. To address these issues, this study proposes a novel combined system that utilizes a suitable scheduling technique for the optimum operation of a wind farm-compressed air energy storage (CAES) system to maximize profit and revenue while maintaining grid frequency. The CAES system’s energy level is divided into four different levels, and an optimal strategy has been developed to efficiently utilize the CAES system to maintain grid frequency. This work has been conducted in both regulated and deregulated environments using a modified IEEE 30-bus system. The proposed method has been compared with an existing approach and has yielded better results in all aspects.

Socio-economic advancement and grid-frequency control of a wind farm-PHS-solar connected EV hybrid electrical system considering imbalance prices

The Independent System Operator (ISO) plays a key role in a competitive electrical system by controlling the market with the received bidding from the different market participants. The ISO settled the energy at the location-based marginal pricing (LBMP) where all the participants get economic benefits. In the renewable combined system, the future renewable power production data need to be submitted to the ISO at least one day before the operation. There is always a chance for not fulfilling the contracted power due to the volatile nature of the renewable which creates an imbalance price in the system. In this situation, energy storage systems can alleviate the imbalance issues by supplying additional power to the electrical grid. This study proposes a two-phase scheduling technique for the optimum operation of a wind farm (WF)–pumped hydro storage (PHS)–solar-connected electric vehicle (EV) hybrid system to obtain more economic profit and revenue with a stable grid frequency. In the first phase, the energy level of PHS upper reservoirs has been scheduled to maintain the grid frequency with the presence of a wind farm. Along with the first phase, additionally solar-connected EVs are incorporated in the second phase to maximize the system profit further to get more economic benefit. A modified IEEE 30 bus system is used to effectively assess the aforesaid approach. MiPower software has been used to conduct this work. This proposed method has been compared with the existing method and got better results in all aspects.

Optimal Electricity Imbalance Pricing for the Emerging Penetration of Renewable and Low-Cost Generators

Problem definition: With the rise of renewables and the decline of fossil fuels, electricity markets are shifting toward a capacity mix in which low-cost generators (LCGs) are dominant. Within this transition, policymakers have been considering whether current market designs are still fundamentally fit for purpose. This research analyses a key aspect: the design of real-time imbalance pricing mechanisms. Currently, markets mostly use either single pricing or dual pricing as their imbalance pricing mechanisms. Single-pricing mechanisms apply identical prices for buying and selling, whereas dual-pricing mechanisms use different prices. The recent harmonization initiative in Europe sets single pricing as the default and dual pricing as the exception. This leaves open the question of when dual pricing is advantageous. We compare the economic efficiency of two dual-pricing mechanisms in current practice with that of a single-pricing design and identify conditions under which dual pricing can be beneficial. We also prove the existence of an optimal pricing mechanism. Methodology/results: We first analytically compare the economic efficiency of single-pricing and dual-pricing mechanisms. Furthermore, we formulate an optimal pricing mechanism that can deter the potential exercise of market power by LCGs. Our analytical results characterize the conditions under which a dual pricing is advantageous over a single pricing. We further compare the economic efficiency of these mechanisms with respect to our proposed optimal mechanism through simulations. We show that the proposed pricing mechanism would be the most efficient in comparison with others and discuss its practicability. Managerial implications: Our analytical comparison reveals market conditions under which each pricing mechanism is a better fit and whether there is a need for a redesign. In particular, our results suggest that existing pricing mechanisms are adequate at low/moderate market shares of LCGs but not for the high levels currently envisaged by policymakers in the transition to decarbonization, where the optimal pricing mechanism will become more attractive.Funding: R. Belo acknowledges funding by Fundação para a Ciência e a Tecnologia [UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab – PINFRA/22209/2016], POR Lisboa, and POR Norte [Social Sciences DataLab, PINFRA/22209/2016].Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0555 .

Optimal Participation of RES Aggregators in Energy and Ancillary Services Markets

With the advent of bulk volumes of renewable energy, RES aggregators have evolved into key market players of the energy industry. This paper presents a holistic approach to derive the optimal offering strategy of a price-taker RES aggregator in the day-ahead energy and ancillary services markets. The day-ahead, the ancillary services and the real-time balancing market functions are modeled in detail. Fundamental imbalance settlement mechanisms are examined, namely Single Pricing, Two Price settlement and Dual Pricing. Several sources of uncertainty are considered affecting the day-ahead market prices, the ancillary services prices, the imbalance prices and the actual production of the RES aggregator. To cope with these uncertainties, a stochastic risk-constrained optimization model is formulated for the aggregator's optimal bidding. A realistic application in the Greek power system allows for examining the motivations for strategic bidding and accurate forecasting in each imbalance pricing scheme. The interplay between the energy and the ancillary services offers is analyzed, to examine the RES aggregator's motivations to bid for ancillary services.

Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices

Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices

Aggregation model of various demand-side energy resources in the day-ahead electricity market and imbalance pricing system

We propose a tool for aggregating a large number of buildings’ electricity demand and photovoltaic (PV) generation, controllable heat pump water heaters (HPWH) and electric vehicles (EV) to minimize the procurement cost in a market transaction consisting of a day-ahead market and an imbalance pricing system. The novelty of this research is that the aggregator's strategy can be modeled including various uncertainties, and the value of multiple demand-side resources (DSRs) can be quantified within realistic market rules. The model shows that the ability to control the imbalance through real-time control of DSRs has significant economic benefits. In the control of HPWH hot water storage operation, the procurement cost can be reduced by 13% compared to nighttime operation when the same number of units are installed as the aggregate target rooftop PV and residential demand. The electric vehicle storage battery charge/discharge control can reduce the total cost by 38% compared to nighttime charging under the same conditions. The impact of the PV-prediction error on the procurement cost was found to be generally larger than those of other parameters, and the market-price-prediction error was found to be considerably large during EV charging and discharging optimization. Furthermore, the simulation confirms that the value of control saturates as the proportion of controllable DSRs increases compared to uncontrollable demand and generation.

Deviations from commitments: Markov decision process formulations for the role of energy storage

We study the energy commitment, generation, and storage problem for a wind power producer who can own and operate a battery for different purposes. We consider two main problem settings: In the first setting, the producer may choose to deviate from her commitments based on the latest available information, using the battery to support such deviations. In the second setting, the producer is required to fulfill her commitments, using the battery as a back-up source. We also consider the special cases of these settings with no battery. In these settings, the producer decides how much energy to commit to purchasing or selling, how much energy to generate in the wind power plant, and how much energy to charge into or discharge from the battery. We formulate the producer’s decision-making process as a Markov decision process (MDP) by taking into account uncertainties in the electricity price and wind speed in a market setting where the price can be negative. We analytically compare the total profits of the two main settings. We then conduct data-calibrated numerical experiments to examine the effects of system components, imbalance pricing parameters, negative prices, and wind availability on the system operations and profits. Using the battery to intentionally deviate from commitments rather than to minimize such deviations improves the total profit by 16.5%, while this change in the role of the battery increases the total imbalance by 16 times, on average, in our experiments.

Mitigation of the Inefficiency in Imbalance Settlement Designs Using Day-Ahead Prices

The design of electricity imbalance pricing mechanisms is internationally controversial. Policies on whether to permit virtual trading for market participants and whether, or how, to impose penalty incentives on the imbalance volumes vary widely. Furthermore, market designs vary depending whether the imbalance prices are obtained directly from real-time trading or based upon the offer and demand functions from the day-ahead energy markets. This paper develops an analytical framework for evaluating designs for imbalance settlement mechanisms and we have selected the Japanese electricity market, which has undergone several revisions in its imbalance mechanism, as a good example to assess such variations. We develop a predictive approach for the imbalance volumes and price densities using two-step quantile regressions and derive a new trading optimization for a virtual trader's arbitrage position. We construct supporting models to estimate prediction errors for renewable power and demand as drivers of imbalance volume. The empirical analysis reveals that even in a mechanism with imbalance penalties based upon day-ahead reference prices, virtual trading may still be beneficial to market participants as well as to the system operator. We also find that greater market transparency is crucial for increased benefits. The insights generalize beyond the Japan case study.

Probabilistic Forecasting of German Electricity Imbalance Prices

The imbalance market is very volatile and often exhibits extreme price spikes. This makes it very hard to model; however, if predicted correctly, one could make significant gains by participating on the right side of the market. In this manuscript, we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 min before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. Therefore, the traders should avoid participating in the balancing market, which is inline with the objective and current regulations of the market. The analysis was conducted on the German market; however, it could be easily applied to any other market of a similar structure.

Predicting Electricity Imbalance Prices and Volumes: Capabilities and Opportunities

Electricity imbalance pricing provides the ultimate incentive for generators and suppliers to contract with one another ahead of time and deliver against their obligations. As delivery time approaches, traders must judge whether to trade-out a position or settle it in the balancing market at the as-yet-unknown imbalance price. Forecasting the imbalance price (and related volumes) is therefore a necessity in short-term markets. However, this topic has received surprisingly little attention in the academic literature despite clear need by practitioners. Furthermore, the emergence of algorithmic trading demands automated forecasting and decision-making, with those best able to extract predictive information from available data gaining a competitive advantage. Here we present the case for developing imbalance price forecasting methods and provide motivating examples from the Great Britain’s balancing market, demonstrating forecast skill and value.

Optimal participation of RES aggregators in electricity markets under main imbalance pricing schemes: Price taker and price maker approach

This paper presents a holistic approach to derive the optimal offering strategy of a RES aggregator in the day-ahead market, considering the aggregator's potential to affect market prices (price-taker vs price-maker). The day-ahead and the balancing market functions are analytically modeled and fundamental imbalance settlement mechanisms are examined, namely Single Pricing and Dual Pricing. Several sources of uncertainty are considered affecting the day-ahead market prices, the imbalance prices and the actual production of the RES aggregator. To cope with these uncertainties, a stochastic risk-constrained optimization model is formulated for the RES aggregator's optimal bidding. For the case of a price-maker aggregator, an iterative algorithm is proposed executing sequentially the market functions and the aggregator's optimal strategy model. A realistic application in the Greek power system allows for examining the motivations for strategic bidding and accurate forecasting provided to the RES aggregator in each imbalance pricing scheme. Reinforcement Learning is applied in the case of high-size RES aggregators under Single Pricing and valuable insights are provided on the convergence of the proposed algorithm.

Optimal offering of wind-photovoltaic-thermal generation company in energy and reserve markets in the presence of environmental and risk analysis

The integration of thermal units with renewable energy sources is increasing to reduce greenhouse gas emissions and fossil fuels consumption. Also, renewable energy sources such as wind farms (WF) and photovoltaic farms (PVF) can be used by Generation Company (GenCo) to maximize their expected profit. Providing the offering curves for each hour under the uncertainties as electricity and reserve prices, an imbalance price ratio for energy deviations and output powers of PVF and WF is the other important issue for the GenCo’s operator. Therefore, an ε-constraint approach is used in this paper to handle the environmental and economic constraints simultaneously in the proposed Wind-Photovoltaic-Thermal Generation Company (WPT-GenCo). On the other hand, a Downside Risk Constraints Technique (DRCT) is used to handle the risk associated with various considered uncertain parameters in the proposed WPT-GenCo. Besides, obtaining robust offering strategies considering a specific level of greenhouse gas emissions is the other target of this paper. Finally, the TOPSIS method is used to select the trade-off solution through the 3-dimension obtained results of expected profit, greenhouse gas emissions, and the risk associated with various uncertainties. Simulation results show that the proposed WPT-GenCo model is scheduled robustly, and the risk level is reduced dramatically, considering a specific level of greenhouse gas emissions.

Multi-stage stochastic programming based offering strategy for hydrogen fueling station in joint energy, reserve markets

Hydrogen fueling stations (HFSs) with onsite hydrogen production systems, which are usually composed of electrolyzers, hydrogen storage tanks and fuel cells, not only supply hydrogen for hydrogen-powered vehicles but also serve as a dispatchable technology that can bid in electricity markets. Except participating in energy market, joining in reserve market can compensate the cost in energy market and increase the total revenue of HFS. This paper proposes a multi-stage stochastic programming model to find the optimal offering strategy of the HFS in energy, reserve markets taking into account a series of uncertainties: day-ahead price, secondary reserve price, system imbalance price and hydrogen demand. Nonanticipativity constraints are employed to guarantee the decisions are made according to the realized uncertainty information up to the present stage. Compared with traditional stochastic programming model, the proposed model adequately considers the sequential bidding decisions with the gradual revealing of the uncertainty over time. Numerical experiments based on one case study indicate that the participation of reserve market greatly increase the revenue of HFS. In addition, the proposed multi-stage stochastic programming model is effective in characterizing the sequential decision.

Equilibrium strategy based policy shifts towards the integration of wind power in spot electricity markets: A perspective from China

As part of China's power industry reforms, developing appropriate market designs for the inclusion of renewable energy in the electricity spot market has been an ongoing challenge. This paper examined the effects of different market rules on wind power producer behavior to identify the policy implications for a renewable-friendly spot market, for which a market equilibrium model was developed to assess power transactions in the day-ahead and balancing markets, with the upper level being wind power producers seeking the best profits, and the lower level being the market clearing process to minimize generation costs. A case study is given, in which different scenarios are explored to assess the effects of bidding regulations and imbalance pricing mechanisms and to simulate the rising wind penetration in power systems. It was found that in the region modeled: more relaxed bidding regulations could provide wind producers incentives by allowing more profitable strategies; the imbalance pricing settings influence market results and producer revenues; and lower spot prices and power generation costs occur if a larger wind share was fed into the power system and could result in a unit revenue decline. Therefore, new policy directions are needed for the development of China's renewable-friendly electricity markets.

Electricity balancing as a market equilibrium: An instrument-based estimation of supply and demand for imbalance energy

Frequency stability requires equalizing supply and demand for electricity at short time scales. Such electricity balancing is often understood as a sequential process in which random shocks, such as weather events, cause imbalances that system operators close by activating balancing reserves. By contrast, we study electricity balancing as a market where the equilibrium price (imbalance price) and quantity (system imbalance) are determined by supply and demand. System operators supply imbalance energy by activating reserves; market parties that, deliberately or not, deviate from schedules create a demand for imbalance energy. The incentives for deliberate strategic deviations emerge from wholesale market prices and the imbalance price. We empirically estimate the demand curve of imbalance energy, which describes how sensitive market parties are to imbalance prices. To overcome the classical endogeneity problem of price and quantity, we deploy instruments derived from a novel theoretical framework. Using data from Germany, we find a decline in the demand for imbalance energy by 2.2 MW for each increase in the imbalance price by EUR 1 per MWh. This significant price response is remarkable because the German regulator prohibits strategic deviations. We also estimate cross-market equilibriums between intraday and imbalance markets, finding that a shock to the imbalance price triggers a subsequent adjustment of the intraday price.

Impact of Imbalance Pricing on Variable Renewable Energies with Different Prediction Accuracies: A Korean Case

Although the Korean government plans to increase its share of variable renewable energies (VREs), the Korean power market is not sufficiently mature to accommodate a large increase in VRE generation. Thus, the Korean system operator plans to introduce a two-settlement, and an imbalance settlement is also under consideration, among several options. Therefore, this study analyzes how many incentives are given for prediction accuracy under several imbalance settlement schemes adopted from European and US power markets. Results show that the imbalance settlement consisting of threshold and penalty terms is useful for rule-makers, who can control revenue differences between the groups with different prediction accuracies by adjusting the two terms. The suggestion given in the paper will be useful for not only the Korean power market but also for the countries that plan to establish the imbalance settlement rules while increasing renewable energy.