Energy Market Model Research Articles

Strategic trading equilibria in local community considering co-existence of pool-based and peer-to-peer energy markets with free-rider problem in network usage cost

The peer-to-peer (P2P) energy trading market is considered a promising method for allocating prosumers’ locally produced power in smart buildings. However, the existing literature overlooks the coexistence of P2P and pool-based energy markets. Therefore, this paper establishes a bi-level energy market model that incorporates both P2P and pool-based markets. The prosumers’ individual profit maximization model considering their network usage costs in the P2P energy trading network at the upper level is first developed. A hybrid market-clearing framework determining the optimal trading amounts with the prosumers’ strategic offers at the lower level is then exploited. The distribution network power flow model in the market clearing framework is then convexified. The lower-level problem is replaced with its strong duality conditions. This bi-level problem can be reformulated as an equilibrium problem with equilibrium constraints (EPEC). This EPEC is then solved after being transformed into an equivalent mixed integer linear programming (MILP) model. Finally, a fifteen-bus system is employed to illustrate the characteristics of the hybrid market equilibrium depicted by this EPEC model. The influences of the prosumers’ strategic offers on the distribution locational marginal prices (DLMPs) and network usage costs are highlighted. Since the prosumers can choose to transmit their energy in the pool-based or P2P markets, the emission and pollution costs caused by power transmission can be minimized. A cleaner energy production is thus guaranteed. The free-rider problem is verified by comparing the network usage charges in the strategic and perfect competition scenarios.

Prosumer Impact on Cellular Power Systems

This paper explores the impact of an increasing number of prosumers in electricity supply systems and investigates how market mechanisms can mitigate the negative effects. The Regional Energy Market Model simulates a supply system based on cellular structures, employing agent-based modeling to capture individual behaviors and simulate real market dynamics. This study includes various supply scenarios, such as a solely photovoltaic scenario and a technically diversified scenario with biogas-fueled combined heat and power units. For each scenario, fixed and flexible pricing scenarios are simulated to analyze their effects. The findings reveal that systems heavily reliant on photovoltaics experience negative effects at certain points due to seasonal limitations, while technically diversified supply scenarios demonstrate fewer drawbacks. Flexible pricing systems stimulate demand in a manner beneficial to the system, creating regional added value, and contributing to the balance between generation and consumption, depending on the supply scenario. However, the study underscores that economic incentives alone are insufficient for balancing generation and consumption. The results highlight the importance of exploring opportunities through the interplay of economic incentive mechanisms and technical possibilities.

A multi‐layer–multi‐player game model in electricity market

AbstractHere, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs.

An intelligent multi-layer, multi-agent MMG framework with amelioration of energy efficiency and future investment outlook, under the deregulated day-ahead and real-time market regime

This paper developed an intelligent multi-agent system (MAS) with a multi-layer framework for multi-microgrids (MMGs) using robust and modern communication patterns for deployed agents to achieve distributed tasks. The MMG paradigm introduces three microgrids (MGs) based on the type of load, working environment, and living habitat: residential, commercial, and industrial. In addition, a day-ahead and real-time model is proposed for day-ahead and real-time signals. Intelligent agents in the multi-layer MAS framework make smart decisions based on multiple algorithms to optimize schedule power and minimize costs, considering demand dispatch and demand response as core components. Maximum renewable energy utilization aims to increase user comfort and reduce greenhouse gas (GHG) emissions. Load agents deployed in each MG ensure maximum efficiency. The proposed framework recommends various tariff rates and tariff adjustment strategies to promote and offer an economic evaluation across the respective indices. To minimize the monopoly of the energy market, an efficient energy market model is developed for the proposed MMG paradigm to maximize the competition by incorporating future and spot-market trading schemes for day-ahead and real-time signals. The comparative analysis indicates optimized results based upon the cost-benefit analysis, cost reduction, power transaction in the market, and maximum utilization of renewable energy resources (RERs).

Design and evaluation of architectural framework for a secured local energy market model based on distributed ledger technologies

AbstractBlockchain‐based local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamper‐proof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the on‐chain features of blockchain with trusted execution environments to develop a transparent, tamper‐resistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.

Designing a decentralized peer-to-peer energy market for an active distribution network considering loss and transaction fee allocation, and fairness

Transactive energy management (TEM) in smart grids is a promising approach for enhancing the participation of consumers in managing their energies and developing decentralized energy market models using peer-to-peer (P2P) transactions. The present paper designs and models a fully decentralized P2P energy market considering the technical constraints of the physical electricity network to redesign electricity markets as consumer-centric markets. As the sellers and buyers trade energy via the electricity grid, allocating power losses and transaction fees to players in P2P transactions is of utmost significance. The proposed market allows prosumers to engage directly in bilateral energy trading without requiring intermediaries. In addition to local P2P markets, the buyer prosumers can purchase energy from the upstream market and participate in the demand response (DR) program during hours of local generation shortage. A fairness index is applied to evaluate the players’ satisfaction with fairness and participation in the proposed market. Finally, an alternating direction method of multipliers (ADMM) is utilized to clear the proposed decentralized market. Numerical studies are performed on a standard IEEE 13-bus distribution network with multiple prosumers. The simulation results verify the effectiveness and feasibility of the proposed decentralized market and its clearing approach.

Day-ahead energy market model for the smart distribution network in the presence of multi-microgrids based on two-layer flexible power management

This study examines the implications of day-ahead energy markets (DA) for flexible power management (FPM) in a smart distribution network (SDN) with multiple microgrids (MMGs). MG sources are coordinated with MG operators (MGOs) in the first layer, and MGOs and SDN sources are coordinated with SDN operators in the second layer. An optimization framework with bilevels is used in the scheme. SDN participation in the wholesale and retail DA energy markets is represented by the upper-level model, equivalent to the second-layer FPM. Based on the network's linear operation and flexibility constraints and active load and source operation models, it minimizes the expected SDN energy cost in the markets mentioned. It has the same formulation as the upper-level problem but concerns MG's participation in the retail DA energy market. This corresponds to the first layer of FPM. After that, a single-level formulation is developed using the Karush-Kuhn-Tucker method, and stochastic programming models uncertainty about load, energy price, renewable generation power, and mobile active load energy demand. In this scheme, two-layer power management between microgrids and the distribution network; simultaneous modelling of economic, operation, and flexibility indices; and simultaneous participation of microgrids and the distribution network in the wholesale and retailer energy markets are the novelties. Ultimately, based on quantitative results, the proposed approach is evaluated in terms of its ability to improve the economic, operational, and flexibility state. The proposed scheme reduces the energy loss in the microgrids and the distribution network by around 43%-67% compared to power flow studies. Voltage drop in the proposed scheme is enhanced by about 40%-47% in the suggested scheme compared to power flow analysis. The economic status of the network is improved by roughly 22% in comparison with power flow studies. Also, the presented scheme can provide almost 100% flexibility conditions; however, it is corresponding to increased energy cost.

Can inflation predict energy price volatility?

Fluctuations in energy prices impact production costs and inflation. This study examines whether inflation data can predict volatility in energy markets. Both inflation and energy market volatility exhibit complex behaviour over time, including structural shifts due to demand and supply shocks. Accounting for differences in data frequencies, we use an extended GARCH model (MIDAS) with Laguerre polynomials for time-varying parameters. The empirical results demonstrate that including low-frequency inflation data enhances energy model predictability particularly during periods of high volatility and extreme price fluctuations. Considering inflation improves forecasting for energy market models, benefiting portfolio management and helping policymakers manage inflation.

PROBLEMS OF ENSURING ENERGY POLICY IN UKRAINE IN WARTIME CONDITIONS

Ukraine’s energy policy has an extensive structure in which all elements interact to function. Management activities in the field of energy policy are not unambiguous and simple, as they require taking into account various aspects of the country’s life. These include resource, technical, economic, environmental, organizational, administrative, managerial, social, innovative, political, geopolitical, security and other aspects, as well as ideological aspects of energy supply for vital needs and functions of the state, society and economy. In addition, energy policy activities can evolve significantly over time in the same country, depending on the dynamics of socio-economic, scientific and technological development, and the transformation of energy market models. In recent years, both Ukraine and the world have seen significant changes in the principles of energy sector regulation, and the range of methods of using energy as a tool for achieving political goals is expanding. In addition, the acceleration of economic processes in the world necessitates a rapid response to changes, which, when considering the transition to greening the economy, should be reflected in the state’s energy policy. It is worth noting that the energy policy created in recent years is specified in the relevant EU directives, which is directly transferred to the legal regulations of the EU states. These regulations significantly create conditions and even force reorientation of energy companies’ strategies, as well as cause changes in the behavior of energy consumers. To a large extent, the implementation of these changes is associated with the application of various types of innovations, especially technological and organizational ones. The purpose of the article is to analyze the problems of ensuring energy policy in Ukraine in wartime. In the course of the study, the following research methods were used: method of analysis, synthesis, systematization, comparison, abstract and logical method, statistical methods of studying economic processes. The article considers the reform of the energy policy of Ukraine, which is an integral part of economic and national security, a necessary condition for the development of the state; analyzes the “New Energy Strategy of Ukraine until 2035: Security, Energy Efficiency, Competition”; considers the basic configuration of the country’s energy policy formula, which will help optimize the energy sector infrastructure in accordance with the European Union’s energy efficiency directives; proposes principles of state regulation in the field of energy policy.

Predictive Analytics for Enterprise Innovation of Retail Energy Market Modeling of Integrated Demand Response and Energy Hubs

Many combined heat and power (CHP) energy hubs work within the following heat load mode in the wintertime to supply the request for heat, and renewable energy has been often restricted in the unified energy network (UEN) markets. The power Internet of Things (PIoTs) has enabled UEN to transmit data increasingly frequently. As a result of flexible connections among various UEN networks, renewable energy increases its accommodation capacity considerably. Thus, the purpose of the study is to optimize UEN within the backdrop of PIoTs. According to the impact of PIoTs on UEN, this paper develops the combined demand response (DR) process and the layout of the important parts of UEN. Afterward, this study develops a bi-level economic dispatching process based on the cyber-physical systems of PIoTs and UEN. In the dispatching process, the higher level optimizes the total UEN function; the lower level optimizes the demand-side equipment output and combined DR. Then, the gray wolf optimization scheme is used to solve the bi-level dispatch. Lastly, the standard UEN and the practical network have been used to verify the efficiency of the suggested process.

Overview of Some Recent Results of Energy Market Modeling and Clean Energy Vision in Canada

This paper overviews our recent results of energy market modeling, including The option pricing formula for a mean-reversion asset, variance and volatility swaps on energy markets, applications of weather derivatives on energy markets, pricing crude oil options using the Lévy processes, energy contracts modeling with delayed and jumped volatilities, applications of mean-reverting processes on Alberta energy markets, and alternatives to the Black-76 model for options valuation of futures contracts. We will also consider the clean renewable energy prospective in Canada, and, in particular, in Alberta and Calgary.

A pricing game strategy with virtual prosumer guidance in community grid

AbstractThe peer‐to‐peer (P2P) energy trading model is considered to be one of the appropriate models for managing local energy trading in community grids. But free pricing mechanisms can lead to energy wastage and long response times. In order to facilitate the local consumption of renewable energy within the community, this paper proposes a pricing game strategy with virtual prosumer (VP) guidance on a P2P energy market model in community grid. To prevent market disruption caused by free pricing strategies in the P2P market and ensure the overall social benefit of the community, a VP controlled by the community microgrid manager is introduced to regulate the energy trading of the entire community. A novel and efficient prosumer trading model is proposed where the VP changes its pricing strategy to guide other prosumers to achieve Nash equilibrium and improve the internal consumption rate of new energy in the community. The proposed strategy's rationality and effectiveness are verified through case studies.

Decentralized peer-to-peer model of energy trading in smart grid considering price differentiation

An increase in electricity demand has sparked a new trend to restructure energy markets as consumer-centric marketplaces. One of the possible techniques for establishing decentralized energy market models is peer-to-peer (P2P) energy trading. In P2P energy trading, market players are encouraged to trade energy among themselves through direct negotiations. This paper presents a decentralized P2P model of energy trading considering price differentiation in the smart grid. Price differentiation offers different pricing for different market players based on their demands and preferences. Here, market participants exchange energy at decided price to optimize their welfare without exceeding constraints. Simulation studies are used to analyze the effectiveness of the proposed approach. To validate the efficiency of the proposed method, P2P energy trading with price differentiation results are compared with the results of P2P energy trading without price differentiation. It is clearly observed that the social welfare of the market players with price differentiation is increased by 49.64%

A Mycorrhizal Model for Transactive Solar Energy Markets with Battery Storage

Distributed market structures for local, transactive energy trading can be modeled with ecological systems, such as mycorrhizal networks, which have evolved to facilitate interplant carbon exchange in forest ecosystems. However, the complexity of these ecological systems can make it challenging to understand the effect that adopting these models could have on distributed energy systems and the magnitude of associated performance parameters. We therefore simplified and implemented a previously developed blueprint for mycorrhizal energy market models to isolate the effect of the mycorrhizal intervention in allowing buildings to redistribute portions of energy assets on competing local, decentralized marketplaces. Results indicate that the applied mycorrhizal intervention only minimally affects market and building performance indicators—increasing market self-consumption, decreasing market self-sufficiency, and decreasing building weekly savings across all seasonal (winter, fall, summer) and typological (residential, mixed-use) cases when compared to a fixed, retail feed-in-tariff market structure. The work concludes with a discussion of opportunities for further expansion of the proposed mycorrhizal market framework through reinforcement learning as well as limitations and policy recommendations considering emerging aggregated distributed energy resource (DER) access to wholesale energy markets.

STRATEGIC MANAGEMENT OF THE PORTFOLIO OF FINANCIAL ASSET

Until now, three approaches to asset portfolio management have been used. The first approach is the classic one, based on the "Efficient Market Hypothesis" (EMH). The second and more modern approach is related to the "Fractal Market Hypothesis" (FMH). Modern economic practice is characterized by the presence of structurally unstable markets, included as nodes in the network of the world economy, which functions in real time. The structure of the available financial instruments is heterogeneous and non-Markovian processes arise in them. The third approach is the formation of a dynamic strategy of investment management of the asset portfolio. Due to the complex structure of the modern global financial market, the heterogeneous structure of available financial instruments and traders using different approaches and time horizons, forecasts, as a rule, require a large number of observations, work poorly at the edges of bifurcations, and do not have a computer model that could build forecasts in real time. In these structures, slow diffusion-type processes with a memory phenomenon occur, i.e., non-Markovian processes. Therefore, the formation of a dynamic strategy using modern methods of mathematical and computer modeling is very promising.
 Real financial data is expressed in rational numbers. Computational models were developed on the basis of classical substance analysis. Therefore, p-adic analysis methods are increasingly used in financial mathematical modeling. These methods are used, in particular, in the construction of neural networks, cellular automata, and percolation models. The paper seems to have taken the first step towards building a "synthetic" model of dynamic asset portfolio management. The model has the form of a differential equation in fractional derivatives obtained using the so-called interbasin kinetics method. A general form of the energy market model is also offered, as one of the specific, especially nowadays, markets.

A proposed hedge-based energy market model to manage renewable intermittency

Renewable energy power producers are exposed to significant volatility in revenue due to intermittency associated with wind and solar energy. Moreover, higher penetration of variable renewable energy in the electric grids is significantly increasing the cost associated with procuring reserves, resulting in higher electricity costs to consumers. Hence, a market that can appropriately distribute the cost of procuring reserves whilst ensuring revenue stability for renewable energy producers is crucial. In this work, the authors propose a novel hedge-based energy market model that allows renewable generators to secure hedge contracts from flexible generating technologies as insurance against weather-driven energy deficits. The proposed model supplements a representative day-ahead market model and maximizes the revenue of market participants whilst diminishing the costs of procuring reserves and generating investment signals for green projects. A mathematical model is formulated to determine market equilibrium based on the Karush Kuhn Tucker (KKT) optimality conditions. Simulation studies are carried out to demonstrate the efficacy of the proposed model on a test network using MATLAB. The theoretical results are verified by simulation results and provide a feasible region in which mutually acceptable hedge contracts result in higher overall revenues. The results show that a hedge-based energy market model can be deployed to manage renewable intermittency in a day-ahead energy market model to address the risk management needs of renewable power producers.

Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review

Electricity generation using distributed renewable energy systems is becoming increasingly common due to the significant increase in energy demand and the high operation of conventional power systems with fossil fuels. The introduction of distributed renewable energy systems in the electric grid is crucial for delivering future zero-emissions energy systems and is cost-effective for promoting and facilitating large-scale generation for prosumers. However, these deployments are forcing changes in traditional energy markets, with growing attention given to transactive energy networks that enable energy trading between prosumers and consumers for more significant benefits in the cluster mode. This change raises operational and market challenges. In recent years, extensive research has been conducted on developing different local energy market models that enable energy trading and provide the opportunity to minimize the operational costs of the distributed energy resources by promoting localized market management. Local energy markets provide a stepping stone toward fully transactive energy systems that bring adequate flexibility by reducing users’ demand and reflecting the energy price in the grid. Designing a stable regulatory framework for local electricity markets is one of the major concerns in the electricity market regulation policies for the efficient and reliable delivery of electric power, maximizing social welfare, and decreasing electric infrastructure expenditure. This depends on the changing needs of the power system, objectives, and constraints. Generally, the optimal design of the local market requires both short-term efficiencies in the optimal operation of the distributed energy resources and long-term efficiency investment for high quality. In this paper, a comprehensive literature review of the main layers of microgrids is introduced, highlighting the role of the market layer. Critical aspects of the energy market are systematically presented and discussed, including market design, market mechanism, market player, and pricing mechanism. We also intend to investigate the role and application of distributed ledger technologies in energy trading. In the end, we illuminate the mathematical foundation of objective functions, optimization approaches, and constraints in the energy market, along with a brief overview of the solver tools to formulate and solve the optimization problem.

Case study of photovoltaic power plants in a model of sustainable university in Brazil

Universities play an important role in the search for a sustainable environment. The challenge for universities is to outline models of a sustainable society and show their benefits, seeking the engagement of all. In this opportunity, the University of Campinas (UNICAMP), with support from CPFL Brazil (Local Electricity Distributor), created a project called “Sustainable Campus” in August 2017. The Sustainable Campus project encompasses several sub-projects which aim to enhance the sustainability of the university. Among the subprojects (SP) of Sustainable Campus, the subproject photovoltaic (PV) was designed to follow up on the construction and operation of 6 PV power plants gathering a power equivalent of 535.26 kWp. In this way, this work explores the initiatives of the Sustainable Campus project with a focus on PV plants. Thus, it is possible to validate the simulations carried out with the PV plants and verify the impact of the system on the university. For this, the simulator data were compared with the measurement of the PV plants. Assembling the information, it was possible to verify different behaviours in PV plants in a range of 5 km. Quantitatively, for a year of data collected, an actual energy generation of 784.29 MWh was observed, while the simulated result showed a total of 759.04 MWh. PV generation resulted in 1.13% of the energy consumed by the university due to the large size of the campus. Furthermore, as UNICAMP is a consumer that is in the free energy market model, the analysis of a possible financial return is presented. With an estimated payback of 7.65 years considering the average cost of R$ 4.64/Wp, which makes the system viable. Finally, the success of this project aims to bias other universities to deploy renewable energy sources, contributing to sustainable development and scaling the initiative to other sectors.

Blockchain-based decentralized energy intra-trading with battery storage flexibility in a community microgrid system

The growing integration of distributed generations and battery storage equipped with smart meters paves a way to smartly manage the Distributed Energy Resources (DER) using a digital platform to improve the overall performance of the microgrid system. The deployment of distributed energy resources, particularly solar photovoltaic and wind generation, has transformed conventional power consumers into active prosumers. As a result of the expansion and digitalization of power distribution infrastructures, peer-to-peer (P2P) energy intra-trading has evolved as a new paradigm in electricity trade in the community microgrid system. Blockchain is being used to encourage P2P energy intra-trading amongst prosumers, consumers, and Renewable Energy Sources (RES) owners because of its transparency, security, and speed in completing transactions. This P2P concept is further extended in this work to establish a self-sustained community microgrid system in energy trading with a case study to showcase the merits of blockchain technology in providing a secure and effective trading platform for mass users. As a result, the proposed work presents a solution for a secured energy management system that uses blockchain technology to create a decentralized microgrid energy market model that depicts P2P energy transactions with the incorporation of a battery storage system. Again, the microgrid P2P market settles the clearing price considering the probable response of customers when the price varies and incentive is paid to customers and prosumers to change the pattern of using of loads. According to the findings, end-users benefit from energy savings and self-sufficiency due to the combination of automated P2P trade and storage flexibility. Furthermore, a sort of crypto-currency “Cosmos (Atom)” is simulated in the P2P market model and published via the blockchain's mining mechanism. The proposed design framework presented in this paper has addressed the implementation of an effective energy management platform for developing a self-sustained microgrid system using distributed energy resources.

Blockchain-Based Peer-to-Peer Transactive Energy Management Scheme for Smart Grid System.

In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid’s energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches.