Prosumer Community Research Articles

Decentralized energy management regime for prosumer community Microgrids using flexible tube model predictive control

Achieving reliable, cost-effective, and low-carbon energy management for community microgrids (CMs) under suitable computational complexity is challenging due to uncertainties in renewable generation and load demand. To address this challenge, this paper proposes a flexible tube model predictive control-based energy management regime (EMR) for prosumer CMs, enabling robustness against system uncertainties in the energy management strategy with fewer sacrifices in economic performance and computational efficiency. The proposed EMR adopts a multistage receding horizon optimization structure, which allows the prosumers to dynamically adjust their energy scheduling according to the updated energy price and forecast information. Further, a decentralized algorithm with a conditional communication strategy is developed, highlighting less communication burden and private information preservation. Case studies on a typical prosumer CM demonstrate the effectiveness of the proposed method in confronting high system uncertainties. Notably, the proposed method ensures that CMs operate with high reliability (having a maximum energy supply shortage probability of just 0.42 %), in a low-carbon and economical manner (the local accommodation rate of renewable energy generation with low costs reaches 98.83 %), even in scenarios where disturbances occur simultaneously on both the energy supply and demand sides.

Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community

The efficient management of energy communities relies on the solution of the “prosumer problem”, i.e., the problem of scheduling the household loads on the basis of the user needs, the electricity prices, and the availability of local renewable energy, with the aim of reducing costs and energy waste. Quantum computers can offer a significant breakthrough in treating this problem thanks to the intrinsic parallel nature of quantum operations. The most promising approach is to devise variational hybrid algorithms, in which quantum computation is driven by parameters that are optimized classically, in a cycle that aims at finding the best solution with a significant speed-up with respect to classical approaches. This paper provides a reformulation of the prosumer problem, allowing to address it with a hybrid quantum algorithm, namely, Quantum Approximate Optimization Algorithm (QAOA), and with a recent variant, the Recursive QAOA. We report on an extensive set of experiments, on simulators and real quantum hardware, for different problem sizes. Results are encouraging in that Recursive QAOA is able, for problems involving up to 10 qubits, to provide optimal and admissible solutions with good probabilities, while the computation time is nearly independent of the system size.

Game-Based Optimal Aggregation of Energy Prosumer Community With Mixed-Pricing Scheme in Two-Settlement Electricity Market

Game-Based Optimal Aggregation of Energy Prosumer Community With Mixed-Pricing Scheme in Two-Settlement Electricity Market

FUNCTIONS OF THE DISTRIBUTED SYSTEM OPERATOR IN PEER-TO-PEER TRADING IN THE PROSUMER GROUP

The article discusses the topic of mutual trade between prosumers at the same level. By increasing the amount of electricity distributed in the grid, it becomes possible to reduce the flow of electrical power in the grid, which in turn helps to reduce power losses and reduce voltage. This opens up an opportunity for prosumers to become participants in the Energy Market, and for ordinary consumers to receive electricity at prices that are lower than the basic tariffs set by energy supply companies. The article pays attention to the analysis of electricity trade between prosumers and ordinary consumers, as well as the role of distributed system operators in this process. The concept of peer-to-peer trading and the system model are described. The influence of different types of prosumers, their economic incentives and sanctions for violation of the rules is studied. The prosumer model is discussed in detail, including loads, renewable energy sources, battery system for energy storage, and switching equipment. It is noted that the purpose of market regulation is to solve a centralized problem that can be solved by a central operator with access to consumers' private information. The proposed method uses a low-voltage flexibility market in which consumers are connected to a single feeder. They form a community of prosumers among themselves and participate in the flexibility market. Limiting consumers to the level of low-voltage networks reduces flexibility, but allows to increase the efficiency of calculations. In addition, prosumers at the connection point can compete with each other to provide aggregate flexibility at low voltage levels. ORG provides the necessary flexibility of the peer-to-peer market in view of voltage limitations and congestion of distribution networks.

Provision of reactive power services by energy communities in MV distribution networks

The paper presents a procedure for the optimal operation of a community of prosumers connected to a medium voltage distribution network equipped with generation and storage units that considers the penalization for low power factor operation, the exploitation of direct exchanges of both active and reactive power between the prosumers and the provision of reactive power services by the community to the local distribution system operator and the transmission system operator. The proposed procedure calculates the maximum and minimum reactive power deviations that each community participant can provide with respect to the reference profile. A deterministic day-ahead scheduling problem is considered assuming the forecast of load and photovoltaic production known without uncertainties. The formulation of the optimization problems and the solution computational requirements are suitable for the inclusion in a stochastic approach. The effectiveness of the approach is supported by numerical simulations of the daily scheduling for different test cases.

The intelligent modelling and optimization of an economic and ecosystem-friendly model for grid connected prosumer community.

The establishment of grid-connected prosumer communities to bridge the demand-supply gap in developing nations, especially in rural areas will assist to minimize the use of carbon enriched fossil fuels and the resulting economic pressure. In the promoted study, an economic and ecosystem-friendly hybrid energy model is proposed for grid-connected prosumer community of 147 houses in district Kotli, AJK. The grid search algorithm-based HOMER software is used to simulate and analyze the load demand and biomass sources-based onsite collected data through a survey for an optimal proposed design. The research objectives are to minimize the net present cost (USD) of design, the per unit cost of energy (USD/kWh), and the carbon emissions (kgs/year). A sensitivity analysis based on photovoltaic module lifetime is also performed. The simulations show that the per unit cost of energy is reduced from 0.1 USD/kWh to 0.001 USD/kWh for the annual energy demand (kWh/year) of the community. The number of carbon emissions is also minimized from 122056 kgs/year to 1628 kgs/year through the proposed optimal energy model.

A multi-agent reinforcement learning approach for investigating and optimising peer-to-peer prosumer energy markets

Current power grid infrastructure was not designed with climate change in mind, and, therefore, its stability, especially at peak demand periods, has been compromised. Furthermore, in light of the current UN’s Intergovernmental Panel on Climate Change reports concerning global warming and the goal of the 2015 Paris climate agreement to constrain global temperature increase to within 1.5–2 °C above pre-industrial levels, urgent sociotechnical measures need to be taken. Together, Smart Microgrid and renewable energy technology have been proposed as a possible solution to help mitigate global warming and grid instability. Within this context, well-managed demand-side flexibility is crucial for efficiently utilising on-site solar energy. To this end, a well-designed dynamic pricing mechanism can organise the actors within such a system to enable the efficient trade of on-site energy, therefore contributing to the decarbonisation and grid security goals alluded to above. However, designing such a mechanism in an economic setting as complex and dynamic as the one above often leads to computationally intractable solutions. To overcome this problem, in this work, we use multi-agent reinforcement learning (MARL) alongside Foundation – an open-source economic simulation framework built by Salesforce Research – to design a dynamic price policy. By incorporating a peer-to-peer (P2P) community of prosumers with heterogeneous demand/supply profiles and battery storage into Foundation, our results from data-driven simulations show that MARL, when compared with a baseline fixed price signal, can learn a dynamic price signal that achieves both a lower community electricity cost, and a higher community self-sufficiency. Furthermore, emergent social–economic behaviours, such as price elasticity, and community coordination leading to high grid feed-in during periods of overall excess photovoltaic (PV) supply and, conversely, high community trading during overall low PV supply, have also been identified. Our proposed approach can be used by practitioners to aid them in designing P2P energy trading markets.

Peer-to-peer energy trading for photo-voltaic prosumers

This paper reviews a peer-to-peer energy market for prosumers of a regulated utility. Utilities pay retail prices to prosumers regardless of demand, forcing utilities to pass the cost of distribution to other consumers. As prosumers grow in numbers, utilities need a better compensation method. A real-time peer-to-peer energy market application may provide higher incentives. Utilities adopt the service because it would allow for compensation of overhead. By including the utility, a micro-grid is not needed for a peer-to-peer market. The prosumer community can be virtual. The proposed implementation can become an intermediary step to decentralized energy markets. The application shall leverage the real-time demand of consumers and adjust payment instantly to prosumers that can meet that demand. A shared cloud account can allow the community to manage the application and create transparency and trust.

Optimal design and energy management of residential prosumer community with photovoltaic power generation and storage for electric vehicles

The optimum design and management of residential photovoltaic (PV) communities are essential to the reliability to satisfy the demands of communities due to the volatility and intermittence of solar energy. The retailer for energy dispatch management is introduced to improve the economic performances of PV communities through demand-side management measures with the electric grid. This paper proposes a novel two-stage optimization model of design and electricity dispatch strategies for residential PV communities to operate electric vehicles' charges. The first-stage optimization aims to obtain the optimal capacities of PV and batteries. The second-stage optimization optimizes the retailer's hourly electricity prices and electricity dispatch strategies, in which the Stackelberg game is performed between maximizing the retailer's benefit and minimizing the electricity cost of residential basic loads and vehicle charging loads. A case study is implemented to validate the proposed model. The installation capacity of batteries is appropriately increased with the increasing active loads of electric vehicles. The optimal pricing strategies of the retailer based on the trade schemes with the electricity grid are influenced by their ratios of fixed and active loads. Compared to the community without the retailer, the annual electricity cost of users is saved by 21.2 % through the proposed management strategy.

Renewable power distribution networks considering dynamic-differential pricing, carbon trading, and solid waste recycling: A fuzzy programming approach

In the energy market, community of prosumers play a great role to enhance the sustainability of power distribution networks. They generate energy for their own consumption and sell the surplus energy to an electricity company. In this study, a power plant considered communities of prosumers and consumers with the objective to determine the capacity of distributed renewable energy generation units established by the power company itself, the power generation capacity of the prosumers, the dynamic-differential prices offered to the prosumers and consumers, and the optimal buy-back price offered to buy the surplus energy generated by the prosumers. According to the numerical analysis, the prosumers generated 89.82% of the energy demand of the community where the rest of the energy demand is covered by the power from the distributed renewable energy resource units. Both the buy-back energy price that the energy company offered to the prosumers and the net profit that the power company earned increased as the fixed price increased and price elasticity decreased. As a result of its participation in carbon trading by establishing distributed renewable generation units and collecting renewable energy from the prosumers, the power company increased its net profit by 1.13%.

Optimal Charge/Discharge Scheduling for Batteries in Energy Router-Based Microgrids of Prosumers via Peer-to-Peer Trading

The concept of a microgrid of prosumers is demanded for the efficient utilization of renewable energy and the flexible energy trading between prosumers. This paper proposes a novel approach to optimize the charging/discharging schedule of battery energy storage systems in the microgrids of prosumers based on the energy router-based energy sharing structure. The proposed model aims to improve each prosumer's revenue with the arbitrage scheme in the peer-to-peer energy trading market. A novel internal pricing mechanism is presented according to the sharing energy in the prosumer community. A multi-objective evolutionary algorithm is implemented to solve the optimal solutions, and the fuzzy-decision-making method is used to select the schedule from the Pareto frontiers. The simulation results show that each prosumer's profit has been increased under different scenarios compared with the optimal profit of independent operation in the utility grid, with the reduction of the energy interchanged with the grid and the increase of the energy storage system life time. The experimental platform validates the feasibility of the energy router-based energy sharing structure for microgrids of prosumers.

IoT-Based Low-Voltage Power Distribution System Management and Control Platform

In order to improve the operational safety and market operation efficiency of the prosumer energy community, to achieve comprehensive monitoring of abnormalities, fault alarms, and intelligent control and maintenance, to reduce the risk of information security, and to address the many types of operational testing and metering equipment in the prosumer community, the duplication of functions and hardware composition was performed, resulting in the waste of resources of monitoring and metering equipment. In the meantime, we proposed an intelligent perception device-based IoT platform architecture for power distribution communities by integrating the software and hardware of the original operation monitoring and metering equipment of the prosumer-integrated communities. The intelligent perception device for community IoT sensing was first introduced, and then, the operation monitoring and metering equipment in the distribution station area was integrated and optimized to enhance the panoramic state sensing capability of the intelligent terminal; the expansion application direction of data-driven distribution IoT was proposed from the typical application scenario of the terminal.

Bilateral energy-trading model with hierarchical personalized pricing in a prosumer community

With the continuous expansion of distributed energy resources (DERs) in the demand side, the distributed peer-to-peer (P2P) energy-trading market in prosumer community receives extensive research interests. A proper transaction structure and pricing model are inevitable. This paper proposes a bilateral energy-trading structure considering the energy trading agent (ETA) with self-built photovoltaic (PV) and energy storage system (ESS), to provide more sufficient energy transactions in community. A bilateral energy-trading model is also developed to make better use of local resources, including utility maximum model and hierarchical dynamic pricing model for the self-build energy-owed ETA and prosumers. Considering the personalized flexibilities of energy consumption and market contributions, the proposed pricing model focuses on the share of demand and supply market and demand response (DR) discount factor to make differentiated pricing decisions and promote market competitiveness. To solve the model, the distributed alternating direction method of multiplier (ADMM) with the predictor–corrector mechanism is realized. Finally, an example simulation verifies the effectiveness and advantages, that the proposed model can decrease the total cost by 5.11% and increase the ETA’s income by more than 9.38% compared to other designed cases.

Maximizing self-consumption rates and power quality towards two-stage evaluation for solar energy and shared energy storage empowered microgrids

Substantial penetration levels of intermittent and fluctuated renewable energy resources like PV can cause overcapacity and other operational challenges in the grid. Therefore, energy market actors are directed to the feed-in limitations and restrict the installed capacity. Likewise, the declining incentives make energy storage central to increasing self-consumption. However, economic uncertainties arise concerns about the financial feasibility of energy storage investments. This study presents the techno-economic benefits in increasing PV self-consumption using shared energy storage for a prosumer community under various penetration rates. In the first stage, the optimal energy storage allocations were done using the proposed New Best Algorithm and genetic algorithm with Matlab. Then, the technical performance of the proposed method was simulated with year-long using PSS Sincal. In the second stage, the economic feasibility of increasing PV self-consumption using shared energy storage under various penetration rates is evaluated considering residual energy. The effects of incentives are examined in terms of economic indicators such as payback period, net present value, and internal rate of return. The incentives promote prosumers either with or without energy storage to increase self-consumption. As a result, shared energy storage increased self-consumption up to 11% within the prosumer community. Results and sensitivity analysis are given in detail. The proposed method provides significant economic benefits and improved power quality.

Priority-Based Energy Sharing and Management Among Prosumers in Smart Grids

Rapid population growth, the ongoing fourth industrial revolution, rising energy demands, global environmental concerns, and uneconomic non-renewable energy drain intensify the need for prosumer communities to bridge the demand-supply gap in electrical energy. To this end, in this paper, we propose a priority-based energy sharing and management model, considering the prosumer community equipped with a renewable energy manager for power production and energy storage by limiting the dependency on available resources. In the proposed model, surplus power is shared within the community due to constraints of power line capacity for traditional grids. In addition, energy sharing is based on two distinct criteria: single-use and multiple-uses. The objective of the proposed model is to maximize the utilization of surplus power at a low per unit energy cost and obtain the optimum economic advantage. We employ a binary integer programming (BIP) technique to solve the formulated problem. The proficiency of the prosumer is the primary focus of the employed strategy. A weight factor is introduced to estimate the intra-dependency of the demand-supply gap and energy cost. Simulation results show that the demand-supply gap in the prosumer community reduces up to 41.8 % without energy storage system. Additionally, the results also validate that including the energy storage system in the prosumer community further reduces the demand-supply gap to 67.33 %.

Business models for peer-to-peer energy trading in Germany based on households’ beliefs and preferences

With the expansion of distributed energy resources and the phaseout of the feed-in-tariff scheme in Germany, self-consumption and electricity sharing within a community of prosumers are becoming more profitable. This paper derives optimal business models for a sustainable peer-to-peer (P2P) energy trading platform (ETP) in Germany. It examines data from 1618 residential households collected from an online survey, including 1311 consumers and 307 prosumers. Our research aims to better understand under what circumstances these households would participate in a P2P ETP and how business models can support such platforms to create added value for private households. Therefore, households’ beliefs concerning their attitudes, perceived behavioral control, and subjective norms are analyzed according to the Theory of Planned Behavior, and business models are designed correspondingly. In order to evaluate the developed business models’ effectiveness and usefulness, we apply them to fifteen existing pioneer energy communities and platforms in Germany. We find that cost-saving and other financial benefits for households must be considered to be the primary value proposition offered by a service provider. Business models which help households to become more electricity self-sufficient and to consume less electricity from the public grid are the second-most important source of value creation from a household’s point of view. By connecting the business models and the P2P prosuming market model, recommendations for companies, policy-makers, and regulatory authorities are made.

Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

Decentralized Coordination of a Community of Electricity Prosumers via Distributed MILP

In this paper, the decentralized energy management of a community of prosumers is studied. The coordination of the prosumers participating in the community is cast as a mixed-integer linear program (MILP) with local and global coupling constraints. A dual decomposition method with a tightening of the coupling constraints is applied to solve the problem in a decentralized way. The resulting tightened problem provides feasibility guarantees for the obtained solutions. A receding horizon approach that exploits the structure of the problem is proposed to divide the problem in subproblems and solve them sequentially. The proposed approach ensures that the tightened problem is feasible under realistic conditions, whereas the existing methods may result in infeasible problems. Moreover, it may reduce the necessary constraint tightening compared to existing methods. Two setups are evaluated for the organizational structure of the community, one with a community coordinator and one without. Moreover, three different structures of the communication graph are analyzed for the setup without a coordinator. The simulation results demonstrate that the proposed method results in feasible problems that can be solved due to the reduced constraint tightening when other methods are not feasible. In the case of feasible problems, the optimality gap is comparable to that achieved with existing methods and even smaller for communities with a small number of participating prosumers.

A Novel Scheme for P2P Energy Trading Considering Energy Congestion in Microgrid

The advancements in Renewable Energy Sources (RES), increasing trends of distributed generation and proliferation of prosumer community requires an affective utilization of energy in Microgrid (MG) paradigm. Peer-to-Peer (P2P) energy trading mechanism provides a platform where prosumers can participate in energy sharing which is beneficial for all users of MG. Meanwhile, due to the high penetration of RES and distributed generations in distribution system used in P2P energy trading concept, the control and operation of existing grid is lacking the required functionalities. In P2P energy trading, power congestion occurs on distribution system which can limit effective utilization of RES. As RES and power demands are increasing, congestion problem also tends to increase. Smart Metering (SM) infrastructure is used to handle Energy Congestion Problem (ECP) by more effective management of prosumer community in P2P energy trading mechanism. SM monitors power transactions, maintains status of the network and obtains power profiles of prosumers. In this paper, Normalized P2P (NP2P) energy trading scheme is proposed with objective to decrease load on main grid and cost minimization for all users. NP2P is centralized contracting scheme treats all users in Microgrid equal for energy flow and cost implementation. Three different cases are considered to validate efficiency of proposed P2P energy trading scheme. In this research work, ECP due to NP2P model is analyzed, modeled as Knapsack Problem (KP) and solved using Greedy and Simulated Annealing (SA) algorithms. In proposed research, Central Energy Management Unit (CEMU) is responsible for implementation of functions for NP2P model and energy congestion control. To improve the reliability of grid, some prosumers with surplus energy should be disconnected. The results of various considered cases for proposed NP2P scheme and algorithms for ECP are compared to verify effectiveness of the solution.

A Framework for Joint Scheduling and Power Trading of Prosumers in Transactive Markets

The emergence of prosumers, as new players in the distribution networks, provides the opportunity to manage distributed energy resources (DERs) at a local level. However, designing an appropriate framework that empowers prosumers to manage their resources and trade energy with each other is a challenging task. This paper proposes a framework for joint scheduling and power trading of a community of prosumers in a transactive energy market (TEM). Prosumers perform day-ahead energy management to schedule their flexible energy resources to reduce their energy expenses. In the scheduling step, each prosumer indicates the expected surplus/deficit energy, which needs to be traded in different markets as well as the flexibility which can be provided in response to grid operator's requests. Then, in intra-day markets, prosumers can trade energy with either their neighbors in a peer-to-peer (P2P) market or the grid. Also, prosumers can participate in an event-driven flexibility market to respond to the requested flexibility by the grid operator. A distributed price-directed market clearing mechanism is presented, which ensures the privacy of prosumers and the market scalability. Simulation results show that energy trading in the community reduces prosumers energy costs, while the grid operator can utilize prosumers' flexibility for managing grid constraints.