Wholesale Energy Market Research Articles

Automated Machine Learning for Optimized Load Forecasting and Economic Impact in the Greek Wholesale Energy Market

This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an always overbought condition in the Day-Ahead as well as in the Futures Market, the excess energy returns without revenue to the market, and the results are compared with a standard contract in Greece, which stands as the lowest as far as the billing price is concerned. The analysis achieved a mean absolute percentage error (MAPE) of 12.89% as the best fitted model and without using any kind of pre-processing methods.

Hierarchical robust Day-Ahead VPP and DSO coordination based on local market to enhance distribution network voltage stability

As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants.

Community-centric distributed energy resources for energy justice and decarbonization in dense urban regions

Low-to-moderate income (LMI) households suffer from higher energy cost burden as they spend a larger portion of their income on electricity bills than higher-income households due to factors including limited access to renewable energy. Although distributed energy resources (DERs) can be owned by individuals, supposedly enabling energy justice, reality shows otherwise since it requires capital investment and property ownership. To mitigate these barriers, the paper proposes “Community Energy Cells” (CECs) which consists of a group of DERs and controllable loads in proximity, represented by a Cell Aggregator (CA) as a single controllable entity. CECs can operate independently during disruptions, deploy renewable energy to LMI households, and provide multiple value streams for their DERs, including the option to aggregate and sell in the wholesale energy market. A financial case study using National Renewable Energy Laboratory’s (NREL) System Advisory Model (SAM) was performed focusing on select buildings in West Harlem, New York City, with approximate 2.5 MW solar and 15 MW storage potentials. Considering that their CA benefits from investment tax credits, and the costs including installation and electricity purchase, it has a net present value of over $2 M, an internal rate of return of 35 % after 25 years, and sufficient cash flow to cover debt service. These returns are sensitive to key values: battery price and committed capacity for incentive programs for DERs and loads. The realization of CEC has interdisciplinary challenges that the paper elaborates on. Nevertheless, CEC is indispensable for tackling climate change and resiliency while attaining energy justice.

A Novel Dynamic Li-Ion Battery Model for the Aggregated Charging of EVs

Implementing successful aggregated charging strategies for electric vehicles to participate in the wholesale market requires an accurate battery model that can operate at scale while capturing critical battery dynamics. Existing models either lack precision or pose computational challenges for fleet-level coordination. To our knowledge, most of the literature widely adopts battery models that neglect critical battery polarization dynamics favoring scalability over accuracy, donated as constant power models (CPMs). Thus, this paper proposes a novel linear battery model (LBM) intended specifically for use in aggregated charging strategies. The LBM considers battery dynamics through a linear representation, addressing the limitations of existing models while maintaining scalability. The model dynamic behavior is evaluated for the four commonly used lithium-ion chemistries in EVs: lithium iron phosphate (LFP), nickel manganese cobalt (NMC), lithium manganese oxide (LMO), and nickel cobalt aluminum (NCA). The results showed that the LBM closely matches the high-fidelity Thevenin equivalent circuit model (Th-ECM) with substantially improved accuracy over the CPM, especially at higher charging rates. Finally, a case study was carried out for bidding in the wholesale energy market, which proves the ability of the model to scale.

Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning

Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven, highly interconnected, and sector-integrated energy system. Simulation models allow testing market designs before implementation, which offers advantages for market robustness and efficiency. This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants. The learning capability makes the agents highly adaptive, thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions. Through distinct test cases that vary the number and size of learning agents in an energy-only market, we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity. Our method is highly scalable, as demonstrated by a case study of the German wholesale energy market with 145 learning agents. This makes the model well-suited for analyzing large and complex electricity markets. The capability of the presented simulation approach facilitates market design analysis, thereby contributing to the establishment future-proof electricity markets to support the energy transition.

Enhanced Automated Deep Learning Application for Short-Term Load Forecasting

In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge in investments catalyzed by technological advancements across diverse sectors. These evolving challenges have necessitated the emergence of new imperatives aimed at effectively managing energy resources, ensuring grid stability, bolstering reliability, and making informed decisions. One area that has garnered particular attention is the accurate prediction of end-user electricity load, which has emerged as a critical facet in the pursuit of efficient energy management. To tackle this challenge, machine and deep learning models have emerged as popular and promising approaches, owing to their having remarkable effectiveness in handling complex time series data. In this paper, the development of an algorithmic model that leverages an automated process to provide highly accurate predictions of electricity load, specifically tailored for the island of Thira in Greece, is introduced. Through the implementation of an automated application, an array of deep learning forecasting models were meticulously crafted, encompassing the Multilayer Perceptron, Long Short-Term Memory (LSTM), One Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal Convolutional Network (TCN), and an innovative hybrid model called the Convolutional LSTM Encoder–Decoder. Through evaluation of prediction accuracy, satisfactory performance across all the models considered was observed, with the proposed hybrid model showcasing the highest level of accuracy. These findings underscore the profound significance of employing deep learning techniques for precise forecasting of electricity demand, thereby offering valuable insights with which to tackle the multifaceted challenges encountered within the power sector. By adopting advanced forecasting methodologies, the electricity sector moves towards greater efficiency, resilience and sustainability.

Offenses committed on the natural gas market: a new subgroup of economic offenses

The liberalization of the natural gas market in the Republic of Moldova determined the completion of Law no. 108 of 27 May 2016 regarding natural gas in accordance with the provisions of Regulation (EU) No 1227/2011 of the European Parliament and of the Council of 25 October 2011 on wholesale energy market integrity and transparency. The emergence of new economic social relations (viz. social relations regarding the integrity and transparency of the natural gas market) conditioned the completion of the Criminal Code with articles 2442 ,,Manipulation on the natural gas market” and 2443 ,,Abusive use of inside information on the natural gas market”. In the present study the constitutive elements of the offenses provided by these articles are analyzed: the object of the offense; the objective side of the offense; the subjective side of the offense; the subject of the offense. The interpretation of art. 2442 and 2443 of the Criminal Code of the Republic of Moldova is done taking into account the extra-penal reference norms.

Energy market manipulation: Criminal law analysis and signs

Article 42 of the Constitution of Ukraine establishes the principles of preventing abuse of a monopoly position in the market and unfair competition in economic activity. Manipulation of the Energy Market – an act that violates the procedure for state regulation of the electric energy market and the natural gas market approved by the current legislation – poses an increased public danger. The purpose of the study is to investigate objective signs of manipulation in the energy market as part of a criminal offence under Article 222-2 of the Criminal Code of Ukraine. Within the framework of the study, a complex of scientific methods was applied: dialectical, hermeneutical, systemic, formal logical, generalisation, system and structural, method of ascent from the abstract to the concrete, analysis, and synthesis. It is proved that the object of manipulation in the energy market is covered by public relations that control the procedure for state regulation of the electric energy market and the natural gas market approved by the current legislation – the implementation by the state of comprehensive measures to manage the demand, prices, and volumes of the wholesale energy market, prevent abuse and violations in this area. The criminal legal content of the concept of “manipulation” is determined by the deliberate activity of a person(s) who violate the conditions of functioning of the wholesale energy market, is carried out by performing a set of illegal actions, the purpose of which is to obtain financial benefits for themselves and third parties, by changing the price of a financial asset. The essence of manipulation of the wholesale energy market is highlighted through its inherent features, which indicate a violation of the conditions necessary for making transactions on the wholesale energy market and a deviation of the asset price from the real market price in the right direction for manipulators (the presence of a process of overstating (underestimating) the total price of the contract, which leads to the loss of its market status). The practical significance of the study lies in the fact that the analysis carried out is of auxiliary importance for law enforcement practice in solving issues related to the legal qualification of manipulation in the energy market

Using building cooling for load shifting on a 5-minute wholesale energy market

To achieve carbon neutrality there must be a significant increase in the proportion of renewable generation. Energy storage and demand flexibility will accommodate more solar and wind generation. Some energy markets in Australia, northern Europe and the USA have moved or are moving to a 5-min settlement period to increase energy flexibility. Commercial building’s HVAC systems could capitalise on new market conditions through 5-min load shifting strategies that have negligible impact on occupant comfort. Here the effectiveness HVAC load shifting strategies for cost arbitrage in 5-min wholesale electrical energy markets are examined. Five strategies are compared using global zone setpoint adjustment and chilled water leaving setpoint adjustment. Results show significant energy flexibility can be achieved without violating occupant comfort. High priced 5-min settlement periods can be successfully coasted over with no HVAC energy consumption for temperatures under 36 °C. Over a 10 week period the case study achieved 14.2% energy cost savings.

Electric Vehicles Charging Strategy for Optimal Bidding in the Wholesale Energy Markets

The increase in the number of Electric Vehicles (EVs) results in additional stress on the grid system in order to fulfill the EVs charging demand. However, with more EVs integration into the wholesale electricity market, EVs fleet operators can play a big role in balancing the grid by bidding in the electricity markets and providing ancillary services. Therefore, optimal strategies for EVs’ bidding in the wholesale energy markets are becoming a necessity. In this paper, a bi-level optimization model is developed for scheduling the charging of EVs as well as allocating optimal bids capacities in the day-ahead, intraday, and reserve markets. The results demonstrate that the model is successful in charging EVs and obtaining remarkable revenue streams from participating in multiple electricity markets by charging and discharging the EVs.

The Current State of Regulatory Support for the Development of Russia’s Renewable Energy and Its Current Transformation

The article addresses matters concerned with transforming the Russian national RES support system starting with the adoption of amendments to the Federal Law FZ-35 “On the Electric Power Industry” and the first by-laws of the Russian Government. Special emphasis is placed on the changes in the methodology and procedure for supporting RES in Russia that have been adopted in connection with the RF Government’s decision to extend the RES development and support program in the country for the period from 2025 to 2035. The adopted changes in the approaches to supporting RES are considered for both the wholesale energy and capacity market and for retail energy markets in Russia.

Spatial Aggregation of Local Flexibility – Horizon2020 project experiences

With the growth of renewables, the increased interconnection of European grids, the development of local energy initiatives, and the specific requirements on TSO–DSO cooperation as set forth in the different Network Codes and Guidelines, TSOs and DSOs face new challenges that will require greater coordination. The aforementioned measures encourage procurement of services at both the transmission and the distribution level, recognizing that this will enable more efficient and effective network management and will increase the level of demand response and the capacity of renewable generation. Digitalization is a key driver for coordination and active system management in the electricity grid, enabling TSOs and DSOs to optimize the use of distributed resources and ensure a costeffective and secure supply of electricity. It also empowers endusers to become active market participants, supporting selfgeneration and providing demand flexibility. To support the transformation, the INTERRFACE project, started in 2019, will design, develop and exploit an Interoperable pan-European Grid Services Architecture (IEGSA) to act as the interface between the power system (TSO and DSO – transmission system operator, distribution system operator) and the customers, and allow the seamless and coordinated operation of all stakeholders to use and procure common services. This paper describes the approach of one INTERRFACE demonstration, the spatial aggregation of local flexibility and its realization that contributes providing a clear market approach to include local constraints into the already well-established and working wholesale energy market solutions

Impact of grid-scale energy storage systems on energy and frequency regulation Colombian markets

This article proposes a co-optimization model that allows evaluating the simultaneous participation of energy storage systems (ESS) in arbitrage applications, secondary frequency regulation, and transmission investment deferral. The model considers the ESS degradation through an upper-piecewise linear approximation; constraints related to primary frequency energy reserve are also included in the model. The formulation is evaluated using historical data from the Colombian wholesale energy market. Eight case studies corresponding to the possible combinations of ESS participation in the three applications are analyzed. Subsequently, a financial evaluation is carried out for each case study, both from the system and the investor's point of view. The results show that the exclusive use of ESS to provide arbitrage is not economically viable in Colombia, while simultaneously providing secondary frequency regulation and restriction relief services is profitable, both for the system and for an investor agent.

EUROPEAN REMIT REGULATION AS THE LATEST DETERMINANT OF CORPORATE RISK MANAGEMENT STRATEGIES IN ENERGY SECTOR

The purpose of the paper is to summarize and present the fundamental trends of the past few decades related to the financial crisis of 2008 and growing integration of European sovereign economies, including the integration of energy markets, that led to development of regulation of relationships and preventing manipulation in such markets. This regulation has become a powerful new determinant for the European multinational enterprises of the energy sector (MNE) when developing their own risk management strategies. Methodology. The analysis is based on the recent studies of the pan-European power exchange company Nord Pool Group, international organization Energy Community and relevant regulations and recommendations of the European Parliament and of the Council and European Union Agency for the Cooperation of Energy Regulators. Results of the analysis showed the growing demand for transparency and stability in the European wholesale energy markets. This demand was reflected in the EU Regulation 1227/2011 on Wholesale Energy Market Integrity and Transparency (REMIT) that was adopted by the European Parliament and the Council of the EU in 2011. While REMIT Regulation, on one hand, provides reliable and equal conditions for MNE, on the other hand, it increases the risk and burden of REMIT compliance obligations for such MNE. The consequences of misconduct can potentially be serious – both high fines and personal liability of MNE employees. Today the European multinational enterprises of the energy sector must develop the respective risk management strategies to ensure effective governance of this type of risks. Practical implications. MNE participants of the EU energy market should develop efficient risk management strategies to comply with REMIT requirements, in addition to existing commitments on the transparency of the Third Package. The REMIT compliance risk management strategies will support MNE market participants in complying with rules and policies, creating a secure structure for employees of such enterprises and promoting fair and equal conditions for trade, trusting the energy market. Furthermore, proper REMIT compliance risk management strategy will help to avoid or minimize the risk of fines and other regulatory sanctions and potential civil lawsuits. It will also help to avoid or minimize the risk of reputation loss, such as negative media reviews or poor customer experience. Value/originality. The conducted analysis provides deep understanding of the main drivers of the European wholesale energy markets regulations that create a compelling determinant for the risk management strategies of multinational enterprises of the energy sector. Ukraine has committed itself to the Energy Community to implement the REMIT Regulation in its regulatory field, which increases the relevance of developing appropriate multinational enterprises risk management strategies within the country.

Robust prequalification process of a distribution system operator considering N‐1 contingency

A distributed energy resource aggregator (DERA) participates in the wholesale energy market (WEM) on behalf of aggregated distributed renewable energy sources (DRESs). The DERA manages DRESs according to the dispatch signal from the system operator (SO). However, a conventional independent SO that operates the WEM and transmission system does not monitor the distribution system. Therefore, overvoltage or overflow problems can occur during the generation of DRESs in a distribution system in which bids are accepted in the WEM. To address these problems, previous studies proposed the prequalification process. In the process, a distribution SO (DSO), which is responsible for the reliability of the distribution system, reviews DERA's bids before it is submitted to the WEM. However, these studies do not consider the outages of the system components. Therefore, we propose an N-1 contingency prequalification process (N-1 PP). The DSO manages the DERA's bid considering both the uncertainties and system states after the reconfiguration in contingency situations. In the proposed scheme, the DERA's bid is reviewed for severe faults and post-reconfiguration systems. The results indicate that the N-1 PP is more stable and durable than the prequalification process of previous studies.

Grid-Aware Aggregation and Realtime Disaggregation of Distributed Energy Resources in Radial Networks

Dispatching a fleet of distributed energy resources (DERs) in response to wholesale energy market or regional grid signals requires solving a challenging disaggregation problem when the DERs are coupled within a distribution network. This manuscript presents a computationally tractable convex inner approximation for the optimal power flow (OPF) problem that quantifies a feeder’s aggregate DERs hosting capacity and enables a realtime, grid-aware control policy for DERs in radial distribution networks. The inner approximation is derived by considering convex envelopes on the nonlinear terms in the AC power flow equations. The resulting convex formulation is then used to derive provable nodal injection limits, such that any combination of DER dispatches within their respective nodal limits is guaranteed to be AC admissible. These nodal injection limits are then used to construct a realtime, open-loop control policy for dispatching DERs at each location in the network to deliver fast grid services in aggregate. The IEEE-37 distribution network is used to validate the technical results and illustrate use cases.

Stacked Revenues Maximization of Distributed Battery Storage Units Via Emerging Flexibility Markets

Power system flexibility is seen as a key for addressing the management challenges in the future smart grids. Distributed Energy Resources (DERs) can provide the necessary flexibility to: a) maintain a stable frequency and a secure energy supply in an overall system perspective, and b) maintain bus voltages and secure transfer capacities in their local networks. In this context, Flexibility Service Providers (FSPs) facilitate the management of the transmission and the distribution network, while at the same time they optimally exploit their DERs. This work proposes a bilevel model for an FSP owning distributed Battery Storage Units and participating in: i) wholesale Energy, Reserve and Balancing Markets, and ii) a novel distribution-level Flexibility Market. The developed model is applied to the IEEE 33-bus radial distribution system and the results demonstrate that it achieves superlinear gains. Finally, a sensitivity analysis is conducted to study the impact of several externalities on the FSP's decisions.

A locational marginal price for frequency balancing operations in regulation markets

The integration of increasingly more renewable energy resources on the electric grid requires consideration of market design for balancing power intermittency. We propose a new locational marginal price for the frequency regulating reserves currently utilized by system operators to balance random power fluctuations on electric grids. Like the widely used wholesale energy price, our proposed price considers the locational effects of delivering electricity services across a congested transmission system. We further propose a regulation market settlement policy which requires market participants with variable power injections to pay based on their frequency regulation needs instead of their energy needs. The calculation of the proposed price and market design is demonstrated using a test case. The results of the test case are further generalized to larger systems. We find the proposed price is more efficient than current regulation market clearing prices and better aligns regulation market design with the wholesale energy market. The limitations and market implications of this proposal are discussed.

A multi-timescale operation model for hybrid energy storage system in electricity markets

The hybrid energy storage system (HESS) connecting different types of energy storage system (ESS) can be used to handle the several timescale variations of the components in power system. In this paper, a multi-timescale economic scheduling strategy for the HESSs to participate in the wholesale energy and reserve market considering the uncertainty of load and renewable generation. The economic multi-timescale HESS scheduling is a three-level model including day-ahead scheduling, intraday operation, and real-time operation. To better cover the hybrid requirements in the stochastic problem, the operation cycle limits model is introduced to avoid the frequent switching of energy-based ESS as well as the deviation cost of HESS’s capacity is used in the finer timescale to guide the future requirement. An improved algorithm that integrates the Progressive Hedging (PH) and Dual Decomposition (DD) algorithms are used to solve this problem. The proposed strategy can realize the efficient management of HESSs to reduce the energy management complexity and system operation costs with high computation efficiency, which indicates its promising application in the HESS economic scheduling.

Coordinated Operation Strategy for a Virtual Power Plant With Multiple DER Aggregators

This article proposes a coordinated operation strategy for a virtual power plant (VPP) with multiple DER aggregators under the wholesale energy and regulation service market. Considering the market clearing process, VPP operation profit, and DER aggregators’ interests, a tri-layer hierarchical framework for VPP is developed to formulate the bidding plans to the superordinate market operator and the dynamic price incentive curves for the subordinate DER owners cooperatively. By using the discrete price quota curves, big-M method, KKT optimal conditions, and strong duality theorem, the tri-layer problem is transformed into an equivalent and tractable mixed integer linear programming problem. Case study with the 141-bus system is conducted to validate the effectiveness of the proposed approach. The advantages of lower cost with operation security guarantee and higher flexibility are demonstrated by comparing with other models.