Energy Prosumers Research Articles

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

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.

Optimization of a Bayesian Game for Peer-to-Peer Trading among Prosumers under Incomplete Information via a CNN-LSTM-ATT

In modern low-carbon industrial parks, various distributed renewable energy resources are employed to fulfill production needs. Despite the growing capacity of renewable energy generation, a significant portion of the power produced by these renewable resources remains unconsumed, resulting in a waste of resources. Within an industrial park, microgrids that both generate and consume energy resources act as energy prosumers. Peer-to-peer (P2P) trading provides an efficient means of utilizing renewable energy among these energy prosumers, who possess both power generation and consumption capabilities. However, within the current market mechanism, each prosumer retains private information that is not disclosed on the network. To address the issue of incomplete information among multiple prosumers during the decision-making process, we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers. The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads. Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics, which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method. These decisions are aimed at minimizing each prosumer's electricity costs. The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.

Short‐term energy forecasting using deep neural networks: Prospects and challenges

AbstractThis study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

The Sharing Energy Storage Mechanism for Demand Side Energy Communities

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.

A Cooperative Game Approach for Optimal Design of Shared Energy Storage System

The energy sector’s long-term sustainability increasingly relies on widespread renewable energy generation. Shared energy storage embodies sharing economy principles within the storage industry. This approach allows storage facilities to monetize unused capacity by offering it to users, generating additional revenue for providers, and supporting renewable energy prosumers’ growth. However, high investment costs and long payback periods often hinder the development of battery storage. To address this challenge, we propose a shared storage investment framework. In this framework, a storage investor virtualizes physical storage equipment, enabling prosumers to access storage services as though they owned the batteries themselves. We adopt a cooperative game approach to incorporate storage sharing into the design phase of energy systems. To ensure a fair distribution of cooperative benefits, we introduce a benefit allocation mechanism based on contributions to energy storage sharing. Utilizing realistic data from three buildings, our simulations demonstrate that the shared storage mechanism creates a win–win situation for all participants. It also enhances the self-sufficiency and self-consumption of renewable energy. This paper provides valuable insights for shared storage investors regarding optimal design and benefit allocation among multiple stakeholders.

Empowering federated learning techniques for privacy-preserving PV forecasting

Machine learning techniques hold significant potential in providing precise forecasts for residential photovoltaic (PV) time series production. This advancement, however, introduces a critical concern: the need to prioritize and uphold privacy when dealing with sensitive personal data within these machine learning models. Preserving the confidentiality of individual information becomes crucial, and the challenge is amplified by the substantial size of the datasets making the process more intricate. This paper explores privacy-preserving federated learning models in the context of big data-driven residential PV production. By leveraging FL techniques, we propose a framework that enables collaborative model training across decentralized prosumer energy data without compromising sensitive information. Prior to the federated approach, an extensive hyperparameter tuning procedure is executed individually for each Long Short-Term Memory (LSTM) model trained on distinct household data. Afterward, a range of clustering algorithms utilizes the models hyperparameters as the input space, each representing the respective prosumer and they undergo evaluation leveraging well-established indexes as metrics. This method not only optimizes the individual LSTM models but also upholds privacy by ensuring that sensitive data remains decentralized throughout the process. The proposed solution aggregates the weights of locally trained LSTMs using the FedAvg algorithm, whilst integrating the differential privacy aggregator. Our solution underwent experimentation involving thirty energy prosumers, utilizing nearly four years worth of data. Various scenarios were chosen, encompassing both local learning and centralized learning methods.

A novel iterative double auction design and simulation platform for packetized energy trading of prosumers in a residential microgrid

AbstractPacketized energy encapsulates energy into modulated, routable, and trackable energy packets, enhancing the flexibility of managing distributed energy resources and expediting prosumers’ participation in transactive energy markets. In the context of packetized energy trading (PET), energy prosumers are naturally deemed as self‐interested agents seeking to obtain their own benefits. To align with prosumers’ demand, supply, quality of service (QoS), and system‐level social welfare, it is necessary to explore the design of prosumers’ bidding strategies and the market clearing methods, considering prosumers’ utility and the best demand response to markets. This study addresses challenges arising from prosumers’ selfishness and asymmetric preferences by proposing a PET‐oriented iterative double auction (IDA‐PET) design, where prosumers are allowed to iteratively change the bids before the auctioneer clears the market. Moreover, IDA‐PET accommodates system capacity constraints, energy balance, and economic constraints, providing cooperative strategies for both prosumers and the auctioneer. To validate the effectiveness of IDA‐PET, a novel and dedicated co‐simulation platform based on the hierarchical engine for large‐scale infrastructure co‐simulation platform is developed and case studies are conducted within a residential microgrid. The simulation results demonstrate that IDA‐PET can efficiently enhance the revenue of the auction market while meeting prosumers’ QoS requirements.

On the design of the architecture of Distribution Level Flexibility Markets

Distribution Level Flexibility Markets (DLFMs) constitute a timely development towards facilitating bottom-up RES and flexibility investments as well as instantiating a fair and regulated interaction framework among energy sector stakeholders, namely System Operators (DSO, TSO), aggregators, Flexibility Service Providers (FSPs), the recently introduced actor called Flexibility Market Operator (FMO) that operates the DLFM and the end energy prosumers. Moreover, the interaction of DLFMs with existing Transmission Level markets and with distributed energy resources is of special interest. In this paper, we present and analyze three possible ways to integrate DLFMs in today’s regulatory framework, each corresponding to a different market architecture, and we evaluate their implications. A comparative analysis reveals the strengths and drawbacks of each one of them both from a system’s as well as from each individual stakeholder’s perspective. The effects of different distributed RES and flexibility penetration levels are also analyzed through a sensitivity analysis. The comparison of the proposed DLFM architectures, both with each other and the current electricity market paradigm, testifies the necessity and the importance of DLFMs and quantifies the impact that they have in: (i) reducing distribution grid management costs, (ii) guaranteeing distribution service quality, and (iii) increasing the penetration of bottom up RES/flexibility investments.

Game theory-based electricity pricing decisions incorporating prosumer energy preferences and renewable portfolio standard

The implementation of renewable energy-based power generation substantially enhances the greenness of the energy market. Concerns about pricing policies and widespread renewable energy (RE) industries have resulted in ongoing economic conflicts among stakeholders, including power companies (PCs) and different prosumer groups. This study proposes a game theory framework embedded in a pricing model to examine the influences of three-part tariff (3 PT) and renewable portfolio standard (RPS) on electricity pricing. Employing a Stackelberg game, the objective is to maximize stakeholders' profits through a sequential decision-making relationship between PCs (as the leader), and residential and industrial prosumers (as the followers). The findings reveal that the PC can maximize profit by coordinating the 3 PT and RPS, regardless of environmental awareness. Furthermore, given the prosumers’ preference for peer-to-peer (P2P) energy trading, the PC should employ a comprehensive approach encompassing 3 PT, P2P, and buy-back to maximize the total profit. On the energy trading platform, the relationship between residential and industrial prosumers is mutually beneficial, with the P2P scheme allowing residents to sell electricity while the industry approaches a lower electricity price from peer channels. Overall, understanding the energy preferences of prosumers should provide flexible and reliable power supply options for different groups of prosumers.

Analysing just energy transition practices in India, Nepal and Bhutan using a trilogy framework of decarbonisation, decentralisation democratisation

The paper discusses the trilogy framework of decarbonisation, decentralisation and democratisation and its relevance in the implementation of just energy transition agendas in rural communities of Global South countries (GSCs). It argues that just energy transition should be implemented via developing nationally independent but locally driven renewable energy projects in GSCs. To corroborate the trilogy framework, the paper discusses case studies of India’s Panchayat institutions, Nepal’s community renewable projects, and Bhutan’s hydro projects. The paper recommends that the trilogy framework be implemented in GSCs through the concept of ‘energy prosumer communities’.

Decentralized AIoT based intelligence for sustainable energy prosumption in local energy communities: A citizen-centric prosumer approach

The role of prosumers who are consumers who produce, store, and consume energy is vital to the uptake of renewable energies in Local Energy Communities (LEC). However, the integration of prosumers in the smart grid to facilitate bidirectional flows of energy and information depends on intelligent operations of energy systems and flexible structures of the existing energy markets. But existing energy trading mechanisms are faced with issues of trust, privacy, security, and energy pricing determination. Also, there are fewer studies based on a citizen-centric prosumer approach. Thus, there is need to provide reliable solutions that addresses the aforementioned challenges faced by prosumers in LEC. Advancements in disruptive technologies, such as Distributed Ledger Technologies (DLT), Artificial Intelligence (AI), and the Internet of Things (IoT) have transformed a broad spectrum of intelligent systems in smart cities. Therefore, this study examines the integration of AI and IoT as AIoT and DLT towards a citizen-centric prosumer approach for decentralized energy markets trading. Additionally, this article develops an architectural model for energy prosumption in LEC using design science approach based on a user-centred design method that shows a possible implementation concept to support energy sharing and trading in LEC. The architectural model supports trust, data privacy, security, and energy pricing determination using AI and smart contracts to provides real-time energy trading monitoring, easy access, control, and immutable logs to unearth underlying energy demand and supply patterns thereby supporting citizen-centric prosumer approach. Finally, a use case scenario of DLT and AIoT for prosumption operations is presented.

Break down the decentralization-security-privacy trilemma in management of distributed energy systems.

Distributed energy systems encompass a diverse range of generation and storage solutions on the user side, where decentralized management schemes to maximize the overall social welfare are preferred considering their dispersed ownership. However, either security or privacy problems occur in recently proposed schemes. Here we report a decentralized framework leveraging the strengths of blockchain and parallelizable mathematical algorithms to overcome these potential drawbacks. The system owners bid cost functions and operating constraints through masked but coupled management subproblems, which are redistributed by the blockchain to be verifiably solved by competent peers. Such processes are iteratively executed as decisions and shadow prices are exchanged among participants, until an equilibrium is reached. The interactive framework ensures decentralized, privacy-preserving, and secure management of multiple energy sources, and reduces the total cost by 3.0 ~ 7.5% in the test system. Our results benefit the energy prosumers and promote a more active and competitive power grid.

Optimal sizing of off-grid microgrid building-integrated-photovoltaic system with battery for a net zero energy residential building in different climates of Morocco

An optimal sizing of an off-grid microgrid system composed of photovoltaic (PV)/building integrated photovoltaic (BIPV)/battery energy storage installation is undergone for Net Zero Energy Residential Building blocks across six different climates of Morocco. The Particle Swarm Optimization algorithm is used to find the optimal sizing of the system, by considering the hourly spatiotemporal variations in both solar energy availability and energy demand, with the lowest Total Annualized Cost as the objective function and capacities of BIPV and battery as decision variables. The methodology adopted focuses on main load fulfillment through direct PV and BIPV power supply, backed by battery energy storage technology, to continually guarantee self-sufficiency. A key metric, the load cover factor, is introduced to quantify the ratio by which the load demand is satisfied by the solar PV and BIPV systems. The findings show that the optimal sizing of the BIPV system can help to improve the load cover factor by 0.68–2.58 %. Moreover, integrating BIPV system with PV system and Battery leads to a reduction in the Levelized Cost of Energy with approximately 8.7–20.72 %, as opposed to utilizing only the PV system and battery. Depending on the local climate, the levelized cost of energy ranges from $0.366/kWh in Ouarzazate city up to $0.664/kWh in Ifrane city. Lastly, this holistic approach aims to transform the building from its traditional role as an energy consumer to a carbon-free energy prosumer.

A complex mixed-methods data-driven energy-centric evaluation of net-positive households

Following the Paris agreement, different policy incentives aiming at the reduction of carbon emissions have been introduced worldwide. Dwellings that benefit from increased renewables penetration, aiming at achieving net-zero and even net-positive energy balance, are being designed and deployed in different countries. This article presents a design mixed-methods approach, based on collected quantitative and qualitative data, to answer the “what”, “why” and “how” of energy prosumption in net-positive dwellings. We demonstrate the strong influence of domestic routines and dynamic energy import and export pricing on explaining energy-centric deviation from net-positive design ambitions. Findings from net-positive neighbourhood households, equipped with geothermal heating, solar generation and electric vehicles, in Norway further provide actionable insights on demand-side reduction and flexibility in energy consumption and how to achieve true energy net-positive balance. Specifically, our analysis demonstrates a significant gap between actual energy bills and user expectations, and potential energy cost reduction up to 10% on a per-activity basis through demand side flexibility in relation to dynamic tariffs as well as a maximum observed bill reduction of up to 50% compared to the baseline scenario for households not adapting their activities inline with dynamic tariffs.

How Much Does Gender Affect the Behaviour of Using Renewable Energy Resources?

The transition to renewable energy resources is a challenge for Romania, as well as for the whole world. The goal of the European Commission is to accelerate the use of renewable resources to reduce net greenhouse gas emissions by at least 55% by 2030. Romania had in 2022, 40,171 prosumers with the forecast that they will reach 100,000 in 2023. The previous research carried out showed a positive attitude of the household population in Romania towards these energy resources, but also important barriers that considerably slow down the process, Romania being behind the target set by the European Commission. Through this research, we seek to identify how gender differences are reflected in the approach to renewable energy resources to facilitate this transition. The present research covers a gap in the specialized literature by investigating, differentiated by gender, the perception of Romanian residents regarding renewable energy resources. The case study was carried out in 2023 on residents of two cities out of the 266 in Romania, Oradea and Timișoara. The reasons for choosing the two cities are related to their similar economic development, positioning in the same part of the country, and, in particular, their performance in increasing the number of green energy prosumers. A questionnaire survey was carried out with a result of 1098 valid questionnaires with a gender structure that allows a differentiated analysis (42.53 men, 53.18 women, 4.28 unspecified). The internal consistency of the answers was carried out with αCronbach. The data processing methods used are Importance-Performance Analysis, RStudio202207.1+554, and qualitative processing with Atlas.ti22. A hypothesis we tested is whether women are more sensitive to environmental issues and the type of energy produced and consumed or the cost factor is the one that dictates. The results suggest gender-differentiated solutions for raising public awareness and encouraging action. Limits of the paper are found only in the two Romanian cities, the geographical extension of the sample being a possible future research direction.

MODELING OF PROSUMER LOAD PROFILES BASED ON BEHAVIORAL APPROACH

The article analyzes various approaches to the modeling of daily electricity load schedules and proposes a methodology for improving the "bottom-up" behavioral model of the load of household consumers. The method is based on averaging generated random load schedules for a given type of household on a particular day of the year. Averaging is carried out by season and length of daylight. Next, working days and weekends are distinguished in each interval. Despite some differences, this work does not separate holidays, Saturdays, and Sundays to avoid unnecessary details. The selected parameters for dividing the averaging intervals allow you to obtain a relatively compact set of model data and, at the same time, preserve the features of the load on different days of the year and hours of the day. Household load profiles with varying steps of day distribution and the main difference of such distribution are considered. Models that more accurately convey the sporadic nature of prosumer consumption compared to typical load schedules are built for two types of households. A detailed analysis of the averaged load graphs was carried out in comparison with the typical graph and the generated load graphs. Averaged load schedules are suitable for modeling the operating modes and control algorithms of the prosumer energy generation and storage system.

Renewable Energy Proliferation and the New Local Energy Community Paradigm: Analysis of a Case Study in Italy

Renewable energy communities (RECs) have been gaining momentum around the world, as a way to promote sustainable development and combat climate change. These communities are typically composed of individuals, businesses, and organizations that come together to invest in and promote the use of renewable energy sources, such as solar, wind, and hydraulic power. This article focuses on the benefits that renewable energy communities bring to a territory through the diffusion of renewable energy systems, tackling different issues like local depopulation, increasing energy prices, and a lack of jobs, while reducing greenhouse gas emissions and improving air quality. The novelty of this article lies in the results from the first-of-its-kind national call within the Next Appennino program, part of the National Complementary plan, aimed at the formation and proliferation of RECs in the area severed by the 2009 and 2016 earthquakes in Italy, as a way to add social, environmental, and sustainable value to the reconstruction of this territory. The data refer to the municipality of Castelraimondo and Unione Montana dei Monti Azzurri (a consortium of mountain municipalities), both located in the Marche region. Analysis of the consumer and prosumer energy needs, as well as the quantification of the exploitable production from the new renewable generators installed, showed that a total of 6.134 GWh/year can be shared by the community.

Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response

Rising energy demands, economic challenges, and the urgent need to address climate change have led to the emergence of a market wherein consumers can both purchase and sell electricity to the grid. This market leverages diverse energy sources and energy storage systems to achieve significant cost savings for consumers while providing critical grid support for utilities. In this study, an energy management system has been employed to tackle the optimization problem associated with various energy sources. This approach relies on mixed-integer linear programming (MILP) to optimize energy utilization while adhering to diverse constraints, yielding a feasible energy solution. This model is applied to real-world energy system consumption data and forecasts the most cost-effective day-ahead energy plans for different types of loads engaged in demand response. Furthermore, time-based charging and discharging strategies for electric vehicles and energy storage systems are considered, conducting a comprehensive analysis of energy costs across various storage devices. Our findings demonstrate that implementing this model can lead to an 18.26% reduction in operational costs when using lithium batteries and a remarkable 14.88% reduction with lead–acid batteries, particularly when integrating solar power and an EV into the system, while GHG is reduced by 36,018 grams/day for a load of 25 kW in one particular scenario. However, the analysis reveals that integrating wind power is not economically viable due to its comparatively higher operational costs.

Empowering Prosumers in The Energy Sector: Integrating Ai And Blockchain For Enhanced Photovoltaic and Battery Systems

In a world where energy is as precious as the finest diamonds, and the quest for sustainable living is akin to a high-stakes spy game, a new era of energy prosumers emerges from the shadows. This tale unfolds in the quiet corners of ordinary neighborhoods, where unsuspecting homeowners hold the key to a revolution in the energy sector. Our protagonists are private clients, each equipped differently in this energy escapade: some with no photovoltaics (PV) or stationary batteries, others with either PV or batteries, and a few with both, harnessing the sun’s power and the might of modern storage. Like a mastermind plotting in a secret lair, Artificial Intelligence (AI) enters the scene. It’s the unseen hero, working tirelessly to analyze, predict, and optimize. AI, with its intricate web of algorithms, becomes the brain behind the operation, determining the most efficient use of energy, managing storage, and turning every joule of electricity into a strategic asset. But what’s a thriller without a twist? Enter blockchain, the enigmatic and unbreakable technology, ensuring every energy transaction is secure, transparent, and immutable. In this world, blockchain isn’t just a ledger; it’s the trusty sidekick, the keeper of secrets, ensuring that every peer-to-peer energy exchange is a discreet affair, away from prying eyes. Our narrative takes a deeper dive in the ’Implementation’ chapter, a dossier of technical know-how and cunning strategies. Here, the AI and blockchain technologies are not just concepts but active agents in a grander scheme, integrated with PV systems and batteries. They’re not just tools; they’re characters in this play, each with a role, each with a purpose. As the plot thickens, we see the dance of electrons, the exchange of watts and volts, in a marketplace that’s as dynamic as the stock exchange. Homeowners, once mere spectators, are now key players in this game of energy and power. This paper isn’t just a collection of data and theories. It’s a narrative of change, a story of a future where every home is a powerhouse, every homeowner a prosumer. It’s a tale of technology and humanity converging to rewrite the script of energy consumption and production. So, dear reader, brace yourself for a journey into the heart of the energy sector, where AI and blockchain are not just the heroes but the harbingers of a new era. Welcome to the thrilling world of sustainable energy, where every decision, every watt, every battery, tells a story

Ecological, financial, social and societal motives for cooperative energy prosumerism: measuring preference heterogeneity in a Belgian energy cooperative

BackgroundEnergy cooperatives in Europe have become more prominent due to the growing interest in and development of renewable energy sources. Cooperatives are often seen as an instrument in the transition to a low-carbon future. The cooperative prosumer is gaining importance, taking on the role of both consumer and producer by setting up and joining electricity cooperatives as investors, shareholders and clients. As is the case with other cooperatives and social enterprises, there are tensions and trade-offs to be made between social, ecological and financial goals. To navigate these tensions and to work towards becoming a resilient institution, it is important that cooperatives preserve their support base made up of their members, who are both clients and shareholders. This study investigates the preference heterogeneity and motivations of members of a large energy cooperative in Belgium, by using stated-choice data from a Discrete Choice Experiment in combination with self-reported membership motives.ResultsDespite the significant presence of member preferences for participation and democratic voting rights, the financial and above all the ecological motives seem to be most important for being a member of the energy cooperative. Based on the stated-choice data, we classify three member types: the financial, the ecological and the social–societal member type. We find a small discrepancy in motives between members who joined early and members who joined later. Where early members care most about ecological aspects, members who joined later have more societal and financial considerations. In terms of effect size and willingness to pay per kilowatt hour, ecological motives prove to be by far the most important factor for cooperative energy prosumers in our sample. In addition, the latent-class analysis shows that over half of the member sample belongs to the ecological motives class.ConclusionsPreference heterogeneity is present to an extent, but there is an overarching preference for sustainability. Though, cooperatives should be aware that their legal form may not be the only factor that drives membership. Rather, keeping high levels of renewable energy, competitive pricing and being an interesting investment opportunity may be key to cooperatives’ resilience and further development on the energy market.