Residential Prosumers Research Articles

The establishment of a micro-scale heat market using a biomass-fired district heating system

BackgroundLocal biomass potential in Southeastern European countries is relatively high. Nevertheless, biomass residues such as wood leftovers, straw and energy crops are often not properly managed or inefficiently utilised for energy purposes in individual house heating or domestic hot water preparation. This is more relevant in rural areas, where the utilisation of biomass resources is mainly based upon traditional technologies, has low efficiency or is carried out by using individual bases without local energy supply management. Usage of biomass residues in combination with other renewable energy sources is in agreement with the targets of the EU’s Energy and Climate Goals and promotes rural development and a circular economy.MethodsFor this purpose, local heating and domestic hot water preparation demands, as well as the available biomass potentials, were analysed and mapped by using a geographic information system (GIS). A model for analysing the optimal operation of the district heating boiler with a relatively high share of solar energy, which is backed up by either a short- or long-term heat storage, was developed. The model takes the supply and the return temperatures from the DH network into account and decides whether the excess of solar heat produced by the prosumers can be delivered into the network. This reduces heat overproduction and enables a smooth and uninterrupted operation of the system. Such configuration would benefit both the DH Company and the prosumers. The DH Company would have the opportunity to buy cheaper excess heat from the prosumers rather than to start its own and relatively slow biomass boiler.ResultsIn this paper, several scenarios are proposed for the Romanian village Ghelinta. The target village is characterised by a small-scale biomass district heating boiler with thermal storage and prosumers with either solar thermal collectors or locally installed heat pumps. Integration of seasonal thermal storage and local prosumers can smooth out the biomass district heating boiler operation and bring additional socio-economic benefits for the bioenergy village communities. This could be the first step towards the establishment of a micro-scale thermal energy market.ConclusionsAnalysis has proven that the proposed system configuration is socio-technically feasible, even for micro-scale systems, as apparent in the Romanian target village Ghelinta. The main objective of this research is to analyse the implementation of a small-scale biomass and renewable energy-based district heating system and to prove the concept of bioenergy villages from a technical and economical perspective. Furthermore, the role of residential household prosumers has been analysed. Based on outcomes, the transferability of the results is also discussed, while several suggestions for stakeholders who implement such projects were formulated for future research as well.

Adaptive Machine Learning for Automated Modeling of Residential Prosumer Agents

An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift problem.

Challenges in energy policies for the economic integration of prosumers in electric energy systems: A critical survey with a focus on Ontario (Canada)

The accessibility and reducing cost of distributed renewable energy sources are stimulating the emergence of small-scale residential prosumers who can produce and consume electricity. This may lead to various scenarios. Such prosumers may increase the uncertainty of consumption behavior, reduce consumption from the grid, and eventually disconnect from the grid. However, they may remain connected, and their energy potential can provide flexibility for the overall system. Current policy in some jurisdictions promotes disconnection through tax increases, grid charges, and other non-commodity costs. In particular, in Ontario (Canada) only 8.7% of the typical electricity bill covers the cost of energy and power; the remainder subsidizes governmental energy-procurement contracts, compensates the grid, pays for environmental initiatives, and covers other taxes. The situation is aggravated by a lack of a global vision for the energy system and of coordinated actions to achieve this vision. We support the preferred scenario in which prosumers remain connected to the grid. As an alternative to Ontario's current attempts to artificially slow the increase in electricity prices, we present an extended critical survey of energy policies to motivate a thoughtful reconsideration of current schemes for the economic integration of prosumers in the energy system.

Framework of locality electricity trading system for profitable peer‐to‐peer power transaction in locality electricity market

This paper proposes an architecture of locality electricity market (LEM) for peer-to-peer (P2P) energy trading among a group of residential prosumers (consumers and producers) with renewable energy resources, smart meters, information and communication technologies, and home energy management systems in a smart residential locality. Prosumers may sell(buy) their excess generation(demand) in LEM at a profitable prices compared to the utility prices in P2P fashion. In order to manage the trading in LEM, a common portal named as locality electricity trading system (LETS) is introduced. The purpose of LETS is to prepare a trading agreement between the participants by fixing a price for every deal based on the quoted price and day-ahead power trading schedule given by the participants. An enhanced intelligent residential energy management system (EIREMS) is proposed at the prosumers' premises to enable their participation in the day-ahead energy trading process and in real-time scheduling of schedulable loads and battery for reducing the electricity bill with due consideration to the operational constraints and LETS agreement. The performances of proposed LETS and EIREMS are validated through a few case studies on a locality with ten prosumers. The proposed methodology endorses marginal economic benefit for all the participants.

A Power Sharing Methodology Considering Wavelet Transform in a Residential Prosumer with a Hybrid Storage System

Power systems have faced significant changes in recent years due to the integration of renewable energies in the power grid. Thanks to its multiple advantages, the so-called clean energies play an important role in the development of the electrical system, allowing the active participation of end users in energy markets. However, the intermittent nature of these sources has delayed their full integration into power systems; unless operated in conjunction with energy storage systems (e.g. batteries, ultracapacitors, etc.) to smooth out the generation and match the demand. This article presents a power sharing methodology for the exchange of energy between batteries and ultracapacitors in a photovoltaic installation. The case study comprises a hybrid energy storage system, on-site generation and a residential user, where both the load and generation profiles are analyzed using wavelet transform. The high- and low-frequency components of both profiles are used to calculate the energy that is injected into the energy storage system. Results show that by separating the components of the signal in high and low frequencies it is possible to take advantage of the characteristics of each storage technology, extending its life cycle.

Vehicle-to-Home Usage Scenarios for Self-Consumption Improvement of a Residential Prosumer With Photovoltaic Roof

This article proposes a procedure for the control of electric vehicle (EV) batteries, aiming to have an optimal matching between local renewable production, domestic loads, and EV consumption. The procedure starts with the analysis of historical photovoltaic (PV), EV, and domestic load profiles. Load and PV profiles are forecasted using statistical-based algorithms, while the expected patterns of EV usages are forecasted using a combination of statistics and clustering techniques. Then, the forecasted profiles are used to estimate future energy balances trough an optimization process. Finally, the real-time management corrects the forecasting logic and checks the parameters of the EV storage to guarantee its correct and safe operation. Three different EV usage profiles (obtained by the clustering of 215 real users) are shown and their impact on the energy balance of EV–PV–home systems is quantified. The results are finally compared with those obtained with a traditional rule-based logic working without forecasts, by also reporting a detailed analysis of the main aspects having an impact on the results.

Methodology for deploying cost-optimum price-based demand side management for residential prosumers

Electricity networks are experiencing increasing congestion and reliability issues as current generation and transmission infrastructures endeavour to match the supply with demand. The integration of intermittent renewable energy resources such as photovoltaic (PV) systems has led to a large variation in energy production and increased supply uncertainty in power systems. In this context, demand-side management (DSM) schemes can be used to motivate prosumers to refine their energy behaviours by offering them various incentives. This study presents a universally-applied methodology that will promote the deployment of effective price-based DSM for residential prosumers. The proposed methodology can be applied on both prosumers and consumers since the utilization of the net-load profile was found to reduce the percentage of unintended revenues by 15%. Additionally, the effectiveness of the methodology was validated through a pilot-network of 300 residential prosumers with installed rooftop PV systems, and resulted in seasonally dependent peak consumption reduction in the range of 1.03% and 3.19% and a reduction of the overall consumption by approximately 2%. Finally, the conducted cost-benefit analysis demonstrated an overall net-benefit of €4.62mln, over a 15-year period, when considering assumptions that are linked to the costs and benefits of a nationwide deployment of the proposed DSM scheme.

Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management

Enabling P2P energy trading among prosumers (both producers and consumers) is a promising paradigm in the decentralized energy era. It is important to design a fair pricing strategy for energy trading; however, it leads to a complicated problem particularly when multi-energy systems participating the energy trading. This study proposes a trading aiding tool, which is based on a Nash-type non-cooperative game model between residential and commercial prosumers with guaranteed trading fairness. The model is generalized considering commonly used energy supply technologies and various demand-side management measures. The energy cost for both residential and commercial prosumers can be minimized with fair pricing strategies for both electricity and heating trading. Compared to previous research, this study presents a more concise solution to determine the fair prices for multi-energy trading. Through the McCormick relaxation, the complex problem is linearized as a Mixed Integer Linear Programming (MILP) model with significant improvement on computational efficiency. A case study is conducted in Shanghai, China, where a community is modeled as the residential prosumer and connected to the commercial prosumer including three commercial buildings. The results indicate that compared to two prosumers standing alone, enabling energy trading with fairness can achieve 4.9% cost saving; the fair-trading prices for electricity and heating are 0.090 $/kWhe and 0.015 $/kWhh, respectively. Overall, this study proposes an efficient tool to provide insights into optimal infrastructure designs, and further quantify the fair pricing and schedule of demand response during P2P energy trading.

The optimal management of the prosumer’s resources via stochastic programming

This paper deals with the optimal home energy management problem faced by a smart prosumer equipped with PV panels and storage systems. The stochastic programming framework is adopted with the aim of explicitly accounting for the inherent uncertainty affecting the main problem parameters (i.e. generation from renewable energy sources and demands). The problem provides the prosumer with the optimal scheduling of the shiftable loads and operations of the available storage systems that minimizes the expected overall electricity cost. Preliminary results, collected on three different categories of residential prosumers, have shown the effectiveness of the proposed approach in terms of cost saving.

A MILP optimization model for assessing the participation of distributed residential PV-battery systems in the ancillary services market

A novel non-linear stochastic method based on a Mixed-Integer Linear Programming (MILP) optimization model is proposed to optimally manage a high number of photovoltaic (PV)-battery systems for the provision of up and down regulation in the ancillary services market. This method, considers both the technical constraints of the power system, and those of the equipment used by all the prosumers. This allows an aggregator of many residential prosumers endowed with photovoltaic (PV)- battery systems to evaluate the baseline of the aggregate by minimizing the costs related to the electrical energy absorbed from the grid and then to assess the up and down flexibility curves with relative offer prices. As confirmed by simulation results carried out considering different realistic case studies, the method can effectively be used by an aggregator to evaluate the economic impact of its participation in the ancillary services market, both for the aggregator and for its prosumers.

A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading

Bi-directional electricity trading of demand response (DR) and transactive energy (TE) frameworks allows the traditionally passive end-users of electricity to play an active role in the local power balance of the grid. Appropriate building energy management systems (BEMSs), coupled with an optimized bidding strategy, can provide significant cost savings for prosumers (consumers with on-site power generation and/or storage facility) when they participate in such bi-directional trading. This paper presents a BEMS with an optimization-based scheduling and bidding strategy for small-scale residential prosumers to determine optimal day-ahead energy-quantity bids considering the expected cost of real-time imbalance trading under uncertainty. The proposed scheduling and bidding strategy is formulated as a stochastic bi-level minimization problem that determines the day-ahead energy-quantity bids by minimizing the energy cost in the upper level considering expected cost of uncertainty, whereas a number of lower-level sub-problems ensure optimal operation of building loads and distributed energy resources (DERs) for comfort reservation, minimization of consumers’ inconveniences and degradation of residential storage units. A modified decomposition method is used to reformulate the nonlinear bi-level problem as a mixed-integer linear programming (MILP) problem and solved using ‘of the shelf’ commercial software. The effectiveness of the proposed BEMS model is verified via case studies for a residential prosumer in Sydney, Australia with real measurement data for building energy demand. The efficacy of the proposed method for overall financial savings is also validated by comparing its performance with state-of-the-art day-ahead scheduling strategies. Case studies indicate that the proposed method can provide up to 51% and 22% cost savings compared to inflexible non-optimal scheduling strategies and deterministic optimization-based methods respectively. Results also indicate that the proposed method offers better economic performance than standard cost minimization models and multi-objective methods for simultaneous minimization of energy cost and user inconveniences.

An electric power trading framework for smart residential community in smart cities

This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win–win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27–68% under different operating conditions.

Optimal Peer-to-Peer energy sharing between prosumers using hydrokinetic, diesel generator and pumped hydro storage

Abstract In this paper, an optimal Peer-to-Peer energy sharing model between two small prosumers is developed and simulated in the south African context. For this purpose, a case study of a commercial type prosumer sharing power with a residential prosumer, both on the same premise, is used to demonstrate the effectiveness of the model. The commercial prosumer owns a small hydrokinetic system operating in conjunction with a pumped hydro storage while the residential prosumer has a diesel generator. The developed model aims to minimize the resulting cost of energy linked to the diesel generator, while optimizing the power flow between the two prosumers. Using actual data, the developed model has been used to simulate and analyze the complex interaction between the different power sources, the energy storage and the demands within the proposed system sizing and operation constraints. The simulation results show that the power flows can be optimally managed, resulting in a substantial reduction in the residential prosumer's operation cost which can now rely not only on its diesel generator but also on the power shared by the hydrokinetic and pumped hydro system of the commercial prosumer.

Engaging men and women in energy production in Norway and the United Kingdom: The significance of social practices and gender relations

To achieve an energy transition favouring renewable energy in the face of climate change, several countries in the EU region have pursued different economic incentives to encourage citizens to invest in household solar systems. This enables citizens to become ‘prosumers’ who produce electricity for their own consumption and sell excess produced electricity to the central grid supply. Influencing people's energy consumption in this way can potentially reach EU's renewable energy targets, as prosumers add to the stock of renewable energy nationally. Through in-depth interviews with men and women from 28 households in Norway and the United Kingdom, this article explores the process of becoming a prosumer and the energy practices in prosuming households. Drawing on theories of social practice and domestication, the article pays particular attention to how the phases of appropriation, objectification, incorporation and conversion of household solar systems are gendered in the sense that women and men have different economic, social and cultural capital, and to how this influences their interaction with technology in the transition from consumers to prosumers. Viewing prosuming through the gender lens reveals how policies need to be designed to promote new practices that are attractive for a more diverse group than today's standard subsidies and feed-in tariffs if the aim is to increase the number of residential prosumers and transition to a more sustainable and equitable low-carbon energy system.

The FlexNett Simulator

This paper documents research conducted in the Norwegian FLEXNETT project. It describes a new tool that was developed to study the future impact of prosumers with PV panels on the grid in Norway and the potential energy flexibility that lies with residential prosumers. Systematic use of energy flexibility can be an important instrument for managing peak loads and voltage problems in weak power grids. The influx of distributed energy resources can amplify this problem, but also help to resolve it. Self-balancing neighborhoods can be very attractive. This implies that loads related to energy demands can be curtailed and leveled out by different controllable devices or managed by using local energy production in the area to reduce the impact on the general distribution grid. The simulation tool is GIS based and can be applied to study the situation related to a single household, a neighborhood or in a specific transformer area. Unlike similar tools that address production yields over a period, the FLEXNETT Simulator addresses production and energy dynamics down to every 10 minutes. Due to the relatively low solar angle in Norway and rapidly changing weather these dynamics can be very prominent and induce local impact that is specific to a house or a neighborhood. The paper further describes how a recurrent neural network has been used as an engine to produce realistic values for the simulator.

A Survey and Evaluation of the Potentials of Distributed Ledger Technology for Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets

The unpredictability and intermittency introduced by Renewable Energy Sources (RESs) in power systems may lead to unforeseen peaks of energy production, which might differ from energy demand. To manage these mismatches, a proper communication between prosumers (i.e., users with RESs that can either inject or absorb energy) and active users (i.e., users that agree to have their loads changed according to the system needs) is required. To achieve this goal, the centralized approach used in traditional power systems is no longer possible because both prosumers and active users would like to take part in energy transactions, and a decentralized approach based on transactive energy systems (TESs) and Peer-to-Peer (P2P) energy transactions should be adopted. In this context, the Distributed Ledger Technology (DLT), based on the blockchain concept arises as the most promising solution to enable smart contracts between prosumers and active users, which are safely guarded in blocks with cryptographic hashes. The aim of this paper is to provide a review about the deployment of decentralized TESs and to propose and discuss a transactive management infrastructure. In this context, the concept of Proof of Energy is proposed as a novel consensus protocol for P2P energy exchanges managed by DLT. An application of the proposed infrastructure considering a Virtual Power Plant (VPP) aggregator and residential prosumers endowed with a new transactive controller to manage the electrical storage system is discussed.

Organizing prosumers into electricity trading communities: Costs to attain electricity transfer limitations and self‐sufficiency goals

Among household electricity end users, there is growing interest in local renewable electricity generation and energy independence. Community‐based and neighborhood energy projects, where consumers and prosumers of electricity trade their energy locally in a peer‐to‐peer system, have started to emerge in different parts of the world. This study investigates and compares the costs incurred by individual households and households organized in electricity trading communities in seeking to attain greater independence from the centralized electricity system. This independence is investigated with respect to: (i) the potential to reduce the electricity transfer capacity to and from the centralized system and (ii) the potential to increase self‐sufficiency. An optimization model is designed to analyze the investment and operation of residential photovoltaic battery systems. The model is then applied to different cases in a region of southern Sweden for year 2030. Utilizing measured electricity demand data for Swedish households, we show that with a reduced electricity transfer capacity to the centralized system, already a community of five residential prosumers can supply the household demand at lower cost than can prosumers acting individually. Grouping of residential prosumers in an electricity trading community confers greater benefits under conditions with a reduced electricity transfer capacity than when the goal is to become electricity self‐sufficient. It is important to consider the local utilization of photovoltaic‐generated electricity and its effect on the net trading pattern (to and from the centralized system) when discussing the impact on the electricity system of a high percentage of prosumers.

Distributed Optimal Coordinated Operation for Distribution System with the Integration of Residential Microgrids

With the increasing integration of rooftop photovoltaic (PV) generation and plug-in electric vehicles (EVs) into the households at user level, household consumers become prosumers. The coordination between the household prosumers and distribution network (DN) becomes essential to the energy management and optimal operation for both entities. In this paper, the residential prosumer cluster is considered as a residential microgrid (RMG) and a hierarchical DN integration method for the multi-RMGs is presented. A two-level hierarchical distributed optimization model is established based on the analytical target cascading to coordinate the RMGs and DNs. At the RMGs level, each RMG is required to individually optimize the energy consumption scheduling in every household by taking into account the effect of time-of-use electricity price on the demand response of EVs and flexible loads. At the DN level, the optimally coordinated operation problem is formulated as a relaxed optimal power flow model based on the second order cone programming by considering the power flow balance constraints. Case studies on the modified IEEE 33-bus system demonstrate the feasibility and effectiveness of the proposed method by achieving coordinated economic optimality as well as coordinated operating points for all entities.

A Non-Cooperative Framework for Coordinating a Neighborhood of Distributed Prosumers

This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers’ objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers’ supply and demand. The evolutionary nondominated sorting genetic algorithm-III (NSGA-III) is employed to generate a set of feasible nondominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving noncooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.

Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets

Active participation of end users in energy markets is identified as one of the major challenges in the energy transition context. One option to bridge the gap between customers and the market is aggregators of smart homes or buildings. This paper presents an optimization model from the standpoint of an aggregator of residential prosumers who have PV panels, electric water heaters, and batteries installed at home level. This aggregator participates in the day-ahead energy market to minimize operation costs by controlling the settings of flexible devices. Given that energy prices, PV production, and demand have uncertain behavior, appropriate models should be used to include these effects. In the present work, Adjustable Robust Optimization (ARO) is used to include uncertainty in the optimization model, and a comparative study of modifications to this formulation is carried out to determine its potential and limitations. The comparative analysis is performed from the point of view of average cost and risk, after performing Monte Carlo simulation. Simulations show the advantages of using an ARO framework when compared to deterministic approaches and also allow us to conclude about the advantages of using the proposed alternative formulation to find more attractive solutions for an aggregator.