Search Results (540)

With the progress in renewable energy and smart grid technologies, electricity users are evolving into prosumers, capable of both consuming and generating electricity through distributed photovoltaic (DPV) systems. Concurrently, the liberalization of the electricity retail market has prompted retailers to design customized electricity packages based on users’ needs and preferences, aiming to enhance service quality, efficiency, and user retention. However, previous studies have not fully addressed the multidimensional characteristics and electricity consumption behaviors that influence package selection. This paper initially dissects user characteristics across three key dimensions: electricity demand preferences, price sensitivity, and risk tolerance. Therefore, leveraging utility functions and autonomous choice behavior models, we propose two innovative electricity purchase and sale combination packages: a fluctuating pricing package and a discount-based pricing package. Furthermore, we introduce the Self-Adaptive Weight and Reverse Learning Particle Swarm Optimization (SAW&RL-PSO) algorithm to address the complexities of these choices. Simulation results indicate that the methodologies presented significantly enhance user benefits and retailer revenues while also effectively managing electricity usage fluctuations and the challenges of integrating large-scale DPV systems into the electrical grid. Full article

In order to reach climate protection goals at national or international levels, new forms of combined heating and cooling networks with ultra-low network temperatures (5GDHC) are viable alternatives to conventional heating networks. This paper presents a simulation library for 5GDHC networks as sustainable shared energy systems, developed in the object-oriented simulation framework OpenModelica. It comprises sub-models for residential buildings acting as prosumers in the network, with additional roof-mounted thermal systems, dynamic thermo-hydraulic representations of distribution pipes and storage, time-series-based sources for heating and cooling, and weather conditions adjustable to user-specified locations. A detailed insight into an in-house development of a sub-model for horizontal ground heat collectors is given. This sub-model is directly coupled with thermo-hydraulic network simulations. The simulation results of energy balances and energetic efficiencies for an example district are described. Findings from this study show that decentralised roof-mounted solar thermal systems coupled to the network can contribute 21% to the total source heat provided in the network while annual thermal gains from the distribution pipes add up to more than 18% within the described settings. The presented simulation library can support conceptual and advanced planning phases for renewable heating and cooling supply structures based on environmental sources. Full article

This research aims to explore the potential of renewable energy sources in urban planning, focusing on microgeneration technologies, through a structured literature review. A systematic review was conducted using the PRISMA method, encompassing the identification, selection, eligibility, and analysis of studies related to renewable energy microgeneration in urban environments. The findings emphasize key areas such as policy development, energy security, and future scenario projections, with a particular focus on solar energy generation. The review highlights the importance of robust regulatory frameworks and monitoring systems for effectively managing prosumers and ensuring equitable energy distribution. Key challenges identified include the intermittency of renewable energy sources, regulatory complexities, monitoring systems, prosumer management, energy sizing risks, and the lifecycle of microgeneration technologies. The research accentuates the need for outstanding collaboration between academia, industry, and urban planners to accelerate the adoption and implementation of renewable energy solutions. The main conclusion is that such collaboration is essential for addressing challenges, driving innovation, and contributing to the development of sustainable urban energy systems. Full article

This study proposes a multi-objective optimization framework for peer-to-peer (P2P) energy trading in South Korea’s tiered electricity pricing system. The framework employs the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) to optimize three conflicting objectives: minimizing consumer costs, maximizing prosumer benefits, and enhancing energy utilization. Using real microgrid data from a South Korean community, the framework’s performance is validated through simulations. The results highlight that MOEA/D achieved an optimal cost of KRW 32,205.0, a benefit of KRW 32,205.0, and an energy utilization rate of 57.46%, outperforming the widely used NSGA-II algorithm. Pareto front analysis demonstrates MOEA/D’s ability to generate diverse and balanced solutions, making it well suited for regulated energy markets. These findings underline the framework’s potential to improve energy efficiency, lower costs, and foster sustainable energy trading practices. This research offers valuable insights for advancing decentralized energy systems in South Korea and similar environments. Full article

Due to the dynamic development of energy generation in photovoltaic installations, a reliable assessment of their impact on the level of energy losses in distribution networks is needed. For energy companies managing network resources, this issue has a very tangible practical aspect. Therefore, ongoing analyses of the level of electricity losses based on actual measurement data of prosumers are needed. In the paper, the influence of energy introduced by prosumer photovoltaic installations on energy losses in a low-voltage radial line is investigated. The issue is examined from three perspectives: 1. Focused on energy supplied into the low-voltage grid from photovoltaic installations; 2. the installations’ locations; and 3. the product of energy and distance from the power source. Comparative assessments are made of the examined aspects for 87 possible locations of prosumer installations in the tested low-voltage network. An analysis of energy losses is carried out both for the entire analysed network and separately for the line and the transformer. The changes in energy losses are influenced by both the power and the location of the photovoltaic installations. Based on the research findings, functions defining relative changes in energy losses in the low-voltage network are determined. Full article

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences. Full article

This article aimed to assess the use of renewable energy sources (RES) in households in Poland in the context of the Statistics Poland (GUS) research and our survey research. In addition, plans for using renewable energy sources and the willingness of respondents to spend money for this purpose were examined. At the beginning of this article, a theoretical approach to the household as an energy prosumer was presented, and the structure of obtaining energy from RES in Poland was shown. Then, the survey research methodology was presented, including the selection of the sample and the purpose of the survey. The next part of this article concerns the characteristics of the respondents and the buildings they inhabit, as well as statistics on RES used in the surveyed households. The research shows that 12% of the surveyed population was an energy prosumer, and 22% were interested in and planning to invest in RES. Only half of the respondents were ready to spend their money on micro-installations. The results were presented using the documentary and comparative methods. This article uses data from Statistics Poland (GUS) and our survey research conducted among 1112 representatives of households in Poland. Full article

This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time. Full article

The energy transition toward low-carbon electricity systems has resulted in a steady increase in RESs. The expansion of RESs has been accompanied by a growing number of energy storage systems (ESSs) that smooth the demand curve or improve power quality. However, in order to investigate ESS benefits, it is necessary to determine their reliability. This article proposes a four-state reliability model of a battery ESS operating with a PV system for low-voltage grid end users: households and offices. The model assumes an integration scenario of an ESS and a PV system to maximize autoconsumption and determine generation reliability related to energy availability. The paper uses a simulation approach and proposes many variants of power source and storage capacity. Formulas to calculate the reliability parameters—the intensity of transition $\lambda$, resident time $T_i$, or stationary probabilities—are provided. The results show that increasing the BESS capacity above 80% of daily energy consumption does not improve the availability probability, but it may lead to an unnecessary cost increase; doubling the PV system capacity results in a decrease in the unavailability probability by almost half. The analysis of the results by season shows that it is impossible to achieve a high level of BESS reliability in winter in temperate climates. Full article

Nowadays, advanced metering and communication infrastructures make it possible to enable decentralized control and market schemes. In this context, prosumers can interact with their neighbors in an active manner, thus sharing resources. This practice, known as peer-to-peer (P2P), can be put into practice under cooperative or competitive premises. This paper focuses on the second case, where the peers partaking in the P2P platform compete among themselves to improve their monetary balances. In such contexts, the domestic assets, such as on-site generators and storage systems, should be optimally scheduled to maximize participation in the P2P platform and thus enable the possibility of obtaining monetary incomes or exploiting surplus renewable energy from adjacent prosumers. This paper addresses this issue by developing a home energy management model for optimal participation of prosumers in competitive P2P platforms. The new proposal is cast in a three-stage procedure, in which the first and last stages are focused on domestic asset scheduling, while the second step decides the optimal offering/bidding strategy for the concerned prosumer. Moreover, uncertainties are introduced using interval notation and equivalent scenarios, resulting in an amicable computational framework that can be efficiently solved by average machines and off-the-shelf solvers. The new methodology is tested on a benchmark four-prosumer community. Results prove that the proposed procedure effectively maximizes the participation of prosumers in the P2P platform, thus increasing their monetary benefits. The role of storage systems is also discussed, in particular their capability of increasing exportable energy. Finally, the influence of uncertainties on the final results is illustrated. Full article

The bidirectional uncertainty of source and load creates scarcity in the reserve resources of the distribution network. Therefore, it is highly significant for the safe and economic operation of the system to harness spare energy storage capacity from prosumers to provide reserves. This paper proposes a bi-layer optimal scheduling model of “distribution network–prosumer group–prosumer” that considers the flexible reserve resources of a prosumer. The upper layer is the “distribution network–prosumer group” optimization model, in which the distribution network sets the electricity price and reserve price according to its own economic benefit and sends it to the prosumer group and guides it to participate in the scheduling of the resources of the prosumer. The lower layer is the “prosumer group–prosumer” optimization model, where the prosumer group incentivizes the prosumer to adjust its energy storage charging and discharging plans through prices and mobilize its own resources to provide flexible reserve resources. The results show that the optimal method proposed in this paper can fully utilize flexible reserve resources from prosumers, improve the economy of distribution network operations, and reduce the pressure of providing reserves using the upper grid. Full article

This paper aims to develop an optimisation-based price bid generation mechanism for the sellers and buyers in a double-auction-aided peer-to-peer (P2P) energy trading market. With consumers being prosumers through the continuous adoption of distributed energy resources, P2P energy trading models offer a paradigm shift in energy market operation. Thus, it is essential to develop market models and mechanisms that can maximise the incentives for participation in the P2P energy market. In this sense, the proposed approach focuses on maximising profit at the sellers, as well as maximising cost savings at the buyers. The bids generated from the proposed approach are integrated with three different market clearing mechanisms, and the corresponding market clearing prices are compared. A numerical analysis is performed on a real-life dataset from Ausgrid to demonstrate the bids generated from sellers/buyers, as well as the associated market clearing prices throughout different months of the year. It can be observed that the market clearing prices are lower when the solar generation is higher. The statistical analysis demonstrates that all three market clearing mechanisms can achieve a consistent market clearing price within a range of 5 cents/kWh for 50% of the time when trading takes place. Full article

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers. Full article

Distributed generation and sector coupling are key factors for economic decarbonization. Because gas networks have a large storage capacity, they have attracted the attention of power engineers to use them to increase the flexibility and security of supply in the presence of renewable and distributed energy resources. This paper makes the first attempt to integrate the electricity and gas systems to fill available gas storage facilities with synthetic natural gas on a large scale. This synthetic natural gas can then be used to operate gas turbines and to compensate for the fluctuating production of renewable energy sources. The LINK-holistic architecture, which integrates renewable and distributed energy resources, is used in this work. It facilitates sector coupling, which means power-to-gas and gas-to-power, throughout the entire power grid and at the customer level. This work is limited to investigating the power-to-gas process at the prosumer level. The electricity surplus of rooftop PVs is used to produce synthetic natural gas, fed into the gas grid after covering the local gas load. The behaviors of the electricity and gas grids are investigated. Results show that electricity prosumers may also become prosumers of synthetic natural gas. The current unidirectional gas grids should be upgraded with compressors at pressure reduction groups to turn them bidirectional, allowing synthetic natural gas storage in the existing large gas storage appliances after considering the pipes’ linepack effect. The proposed solution could make it possible to fill the underground storage plants in summer, when the electricity and synthetic natural gas production exceed electrical and gas demand, respectively. Full article

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side. Full article