Local Energy Market Research Articles

Local multi-modal energy market for thermal-electric energy systems with consideration of temperature flexibility in heating subnetworks

District heating systems are critical in this transition towards climate-neutral energy systems as they enable a close coupling of thermal and electric energy systems. However, the operation of these multi-modal energy systems and adequate representation of conversion dependencies, such as from heat pumps, remain challenging.To address this challenge, a novel method is presented that incorporates the temperature of subnetworks of district heating systems into the market matching of a local energy market for thermal-electric energy systems. This market-based coordination approach includes a linear formulation of market orders tailored to the needs of consumers, producers, and prosumers in local multi-modal energy systems. Emphasis is put on the temperature flexibility of heat pumps that concurrently participate in both heat and electricity markets. Complementary market commodities are introduced to account for temperature constraints set by the market participants. The physical models of the district heating network and electric distribution grid are formulated linearly and incorporated into the market-matching.The proposed method is applied in a case study representing a German district heating system. We conclude that the temperature flexibility of heating subnetworks, and dependencies involved in energy conversion can be incorporated within a linear market-matching problem.

Hybrid local energy markets: Incorporating utility function-based peer risk attributes, renewable energy integration, and grid constraints analysis

This study proposes a novel hybrid Local Energy Market (LEM) trading framework meticulously designed for efficient energy trading among distribution network participants. The proposed LEM hybrid modeling framework aims to maximize social welfare by considering the behavior of market participants and incorporating network constraints into the transactional framework. To address the consumption behavior of market participants, the concept of the utility function with peer risk attributes is employed, differentiating between flexible and inflexible demands. Additionally, it adeptly manages various generation types, including diesel generators, wind and solar generators, and storage, each characterized by a unique cost function. The investigation extends to the analysis of the Japan Electric Power Exchange market (JEPX), taking into account the residential electricity demand in Tokyo, Japan, by comparing with community-based (CB) and peer-to-peer (P2P) markets and scrutinizing market player behavior across multiple scenarios. These scenarios include comparisons of different market structures, variations in prices, considerations of grid constraints, and diverse weather scenarios. The proposed framework undergoes rigorous evaluation utilizing the IEEE 33-bus and 69-bus standard test systems, with results demonstrating its practicality, superiority of the proposed market compared to the CB and P2P markets, and providing valuable insights into the intricate interplay between market players’ behavior and the dynamics of diverse market structures, price fluctuations, grid constraints, and weather scenarios.

The Economic Impact of Electric Vehicle Infrastructure Development on Local Economies and Energy Markets

Fast Evolving EV technology is dramatically changing both transport and energy. Ensuring that nations worldwide hit their targets in reducing carbon emissions and increasing sustainability of energy has made EV infrastructure development, particularly for charging stations and their integration with grids, part of that transformative process. The paper elaborates on the economic effect of EV infrastructure development on local economies and the energy market at large with a focus on the United States, Europe, and India. This study, through detailed research into the proliferation of EVs and associated demand for charging infrastructure, points out the creation of jobs, additional local business benefits, increased values of real estate, and, more importantly, the impact on the energy market in terms of grid stability and demand for renewable energy.

Dynamic capacity withholding assessment of virtual power plants in local energy and reserve market

The increasing utilization of distributed generation resources led to the formation of active distribution networks and virtual power plants (VPPs), which have changed the paradigms of electrical energy transactions in local energy markets. The VPPs can form capacity-withholding groups and impose market power to gain more profits, which may increase the costs of energy procurements for consumers. This paper presents an algorithm for the local electricity market operator in distribution networks to assess the dynamic capacity withholding of VPPs in the local energy and reserve markets. The main contribution of this paper is proposing indices to evaluate the dynamic capacity withholding of VPPs in energy and reserve markets. The other contribution of this paper is that it also quantitatively analyzes the impact of withholding processes on the flexibility of the distribution network. An optimization process is used to estimate coordinated offers of VPPs in the energy market in order to prevent the formation of withholding groups. The proposed algorithm was assessed for the 123-bus IEEE test system and the energy and reserve dynamic capacity-withholding indices were determined for different operating conditions.

PP-LEM: Efficient and Privacy-Preserving Clearance Mechanism for Local Energy Markets

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers’ reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

OPLEM: Open Platform for Local Energy Markets

Local Energy Markets (LEMs) have been proposed as an effective solution to coordinate distributed energy resources within emerging smart local energy systems. However, LEMs are still at an early stage of development and are not widely implemented yet. Extensible open-source software tools are needed to simulate different market designs and evaluate their feasibility and effectiveness. This paper presents OPLEM, an open-source Python package for modelling and testing LEM designs. It offers a modular and flexible framework to create and test market designs adapted for distribution networks. OPLEM integrates two distinct models—one for designing and simulating local energy markets, and another for network operation. Unlike existing tools, OPLEM platform offers a comprehensive solution that bridges the gap between market design and network operation assessment, enabling stakeholders to gain deeper insights into the interactions between market dynamics and grid performance. Additionally, the inclusion of participant and asset models further enhances the tool’s versatility, allowing for a holistic analysis. Thanks to its modular structure, OPLEM allows the development of customised LEM designs and initially incorporates four popular LEM designs: Time-of-Use pricing market, centralised dispatch with distribution locational marginal pricing, bilateral peer-to-peer market and auction market. By combining market design and network modelling functionalities, the tool empowers users to explore various scenarios and optimise market clearings to enhance both economic efficiency and grid reliability.

Expediting battery investment returns for residential customers utilising spot price-aware local energy exchanges

This paper presents a local energy exchange mechanism to benefit residential customers and retailers under variable wholesale market spot prices. To do so, a peer-to-peer (P2P) trading-enabled local energy market (LEM) is proposed to facilitate frequent energy transactions among participants. The proposed LEM mechanism considers market operational constraints and maximises electricity cost reductions. Further, the proposed LEM optimises the customers' investment return for battery energy storage systems (BESSs) and minimises retailers’ financial losses during higher spot prices in the wholesale market. Finally, a case study is demonstrated using real-world Australian data in which P2P trading is performed between interested buyers and sellers. Two scenarios with high and low wholesale market spot prices are analysed comprehensively considering five metrics such as solar-to-spot (STS); solar-to-load (STL); forward buy contract (FBC)-to-load (FBC-L); spot-to-load (Short); and FBC-to-spot (Long). The simulation results reveal that the customers reap substantial benefits from the proposed LEM in contrast with business-as-usual (BAU), allowing them to expedite BESS investment returns. Also, the proposed LEM helps a retailer decrease the effect of higher spot prices by reducing its unexpected financial losses while improving self-consumption and self-sufficiency in the local energy network.

On grid-serving grid tariff design in Local Energy Markets

Local Energy Markets (LEMs) are virtual market places that allow for energy sharing of prosumers in proximity to each other. In LEMs, participants’ offers and bids are coordinated by a pricing mechanism, which sets price signals for generation and loads. This impacts distribution grid utilization. Dynamic grid fees could be used by the distribution system operator to add price signals for market participants that are beneficial to the distribution grid operation. To develop such a grid-serving LEM design, we propose a framework for the design of alternative grid tariffs as a trade-off between cost-reflective and cost-recovering grid tariffs. We analyse three alternative grid tariff components in the context of LEMs: time-varying energy fees, critical peak pricing (CPP) and capacity fees for power not traded on the LEM. A case study on a synthetically generated grid region shows that while the alternative tariff components can reduce the aggregated peak load of the region by up to 31% with the CPP, the fees can also lead to increased line utilization. Also, while total local added value can be increased with all alternative grid tariffs, especially capacity-based grid tariffs are connected to a strong redistribution of grid costs from flexible to inflexible prosumers.

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.

Technical and Economic Energy Optimization of the Energy Hub System with Considering Peak Demand Pricing in the Local Energy Markets

Technical and Economic Energy Optimization of the Energy Hub System with Considering Peak Demand Pricing in the Local Energy Markets

A Recommendation System for Prosumers Based on Large Language Models.

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home's comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers.

An iterative auction-based method for multi energy trading in a microgrid considering renewable energy uncertainties

Due to the significant strides made in renewable energy generating units, they are now an appealing choice for supplying energy with various benefits. Furthermore, the aggregation of different energy generators and resources and the rise of energy conversion facilities, like combined heat and power (CHP), have paved the way for a new category of multi-energy prosumers. This research presents an innovative model for a local energy market structure consisting of an aggregator, prosumers, electricity, gas, and heat utility grids, and consumers. The interplay between these entities is handled as an auction model with proposed bidding strategies in which each player participates to maximize their own objective function. The numerical analysis reveals the impact of the pricing strategy on the prosumers' willingness to provide more energy during peak demand periods. Furthermore, the study highlights the potential for mutually beneficial collaborations between prosumers, which leads to reduced energy costs.

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

ETradeChain: Blockchain-Based Energy Trading in Local Energy Market (LEM) Using Modified Double Auction Protocol

ETradeChain: Blockchain-Based Energy Trading in Local Energy Market (LEM) Using Modified Double Auction Protocol

Dynamic Operating Envelope-Based Local Energy Market for Prosumers With Electric Vehicles

Dynamic Operating Envelope-Based Local Energy Market for Prosumers With Electric Vehicles

New markets for local, renewable, or affordable energy? A segmentation analysis to identify consumer preferences for decentralized energy markets

Local Energy Markets (LEMs) allow direct citizen involvement in energy production and trading. A study surveyed 636 Swiss electricity consumers using choice-based conjoint analysis to explore preferences for various LEM characteristics. Three consumer segments emerged: 'Renewable energy enthusiasts' (42 %), interested in energy mix and community PV investment; 'Price sensitives' (30 %), driven mainly by costs; and 'Uninvolved' (28 %), focused on energy mix but hesitant about PV investment. Demographic and psychological variables helped characterize these segments, suggesting tailored communication strategies to boost participation in future energy markets. Communication strategies should focus on ‘Renewable energy enthusiasts’ segment and should emphasize environmental benefits and community engagement to leverage their high motivation for involvement.

Distributed stochastic energy coordination for residential prosumers: Framework and implementation

This paper addresses the problem of energy coordination among customers in a local energy market to meet heating needs. Accordingly, this paper proposes a demand response framework for the energy coordination of multiple residential prosumers in a hierarchical coordination game-theoretic scenario. That consists of a benevolent coordinator and multiple residential prosumers integrated with solar PV under demand uncertainties as a consequence of forecasting errors in weather data and non-shiftable load. The coordinator and customers are considered to be a leader and various followers, respectively. The coordinator employs a cost-shared strategy to reduce peak demand. Customers maximize individual welfare by employing stochastic programming through demand flexibility realized via electric baseboard heaters and electric thermal storage. Subsequently, the proposed strategy is implemented through a cyber–physical infrastructure incorporating a distributed computing platform with embedded systems and extensive simulations using accurate weather and real-life energy consumption data. A comparative analysis is performed using a deterministic baseline with perfect information, i.e. no forecasting errors. In addition, the proposed mechanism is tested on an adapted CIGRE low-voltage benchmark system. Simulation results demonstrate the techno-economical feasibility of the proposed solution in uncertain environments. Furthermore, they recommend practical strategies for short-term planning of grids highly penetrated by renewables and intelligent devices by increasing their flexibility and leveraging salient features of cyber–physical systems.

Implementation of an Optimal Energy Management within Islanded Microgrid

This paper proposes an optimal Energy Management System (EMS) based on Mixed Integer Linear Programming (MILP) optimization technique for an islanded Microgrid (MG). A local energy market (LEM) platform is employed which attempts to obtain the best electricity market price and to maximize the utilization of existing distributed energy resources. Besides, a single side auction market structure is used for the LEM which takes the offer prices from all microsources to calculate market clearing price. Simulation and experimental results from the proposed method over a real MG are compared with those from a Modified Conventional EMS (MCEMS). The proposed algorithm is flexible, extendable and too fast to be utilized for different MG structures with various capacities. The results demonstrate the effectiveness of the proposed method to solve the optimal operation problem in an isolated MG with 25% reduction in the cost of operation.

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.

Comparative analysis of bidding strategies for auction-based local energy markets

In view of the ongoing integration of distributed energy resources (DERs) and energy storage into the energy system, conventional consumers are transitioning into prosumers and flexumers. Local energy markets (LEMs) enables these end users to trade electricity directly with each other in order to obtain lower energy prices and to increase the local self-consumption. Since bidding strategies have a decisive impact on efficient trading in auction-based LEMs, the comparison and consistent evaluation of bidding strategies is an important research area. As novelty we propose an uniform evaluation methodology keeping all market features equal except the used bidding strategy. For the first time we evaluate a device-oriented (DO) strategy including an incentive price signal with a zero-intelligence (ZI) strategy, a learning-intelligence (LI) strategy employing the modified Roth–Erev learning algorithm. As evaluation we analyze the resulting market trading with various key performance indicators (KPIs), which consider both cost savings and local energy supply. Our findings reveal that the chosen bidding strategy has a decisive impact on cost savings and the distribution of gains between the buyer and seller side. In a photovoltaic (PV) and combined heat and power (CHP) scenario with different technology penetrations, the end users’ gains attain the highest values for the DO bidding strategy with 7909 € and 16454 €. Also the average market clearing prices are for the DO bidding strategy the highest with 0.269 €/kWh for the and 0.3032 €/kWh, which implies that the seller side predominantly obtains the gains.