Search

Renewable energy communities foster the users' engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects. [ABSTRACT FROM AUTHOR]

In Europe, the recast of Directive 2018/2001 defined Renewable Energy Communities as innovative configurations for renewable energy sharing between different end user types. In this regard, this work aims to assess the benefits following the constitution of a Renewable Energy Community in the industrial area of Benevento (South of Italy), involving a mixed-use building and an industrial wastewater treatment plant. The alternative single end users' configuration has been also examined, and both solutions have been compared with the current state where the users' electric energy requests are fully met by the power grid. The users have been equipped with a 466 kWp photovoltaic plant, modelled in HOMER Pro®, providing in input experimental meteorological data (global solar radiation and air temperature) collected by one of the weather control units in Benevento. Real data about users' electric energy demand have been gathered from their electricity bills, and when unavailable their electric load profiles on an hourly basis have been reconstructed based on the aggregated monthly data. Energy sharing has been proven to increase energy self-consumption and the users' self-sufficiency. Annually, the primary energy demand is reduced by 577 MWh (1.2 MWh/kWp), carbon dioxide emissions by 84 tCO2 and operative costs by 101 kEUR. [ABSTRACT FROM AUTHOR]

It is of great significance to introduce the conception of a sharing economy into the electricity industry, which can promote the dispatch of multiple integrated energy systems. On the one hand, it is difficult to reveal the behaviors of complex players with multi-energy coupling through the traditional centralized optimization method of single electric energy. On the other hand, the uncertain fluctuations of renewable energy, such as wind power and photovoltaic, have posed great challenges to market transactions. First, the relationship and the functions of all stakeholders in the system are described in this paper, followed by the establishment of flexible resource models such as demand response and energy storage devices. On this basis, a low-carbon dispatching framework of multiple regional gas–electric integrated energy systems is then constructed under the guidance of cooperative game theory. The contribution indexes are established to measure the degree of energy sharing among the subsystems, and the method of asymmetric Nash bargaining is used to settle the interests of each subsystem. Second, a robust optimization model of multiple regional systems is established in response to multiple uncertainties from renewable energy and load. Finally, the numerical example proves that the proposed mechanism can increase the benefits of each integrated energy system player. Moreover, it helps the system to yield optimal benefits in the face of uncertainties and provides a reference on how to realize energy sharing under uncertainties from source load. [ABSTRACT FROM AUTHOR]

Citizens are set to play an active role in the energy transition by transforming from 'passive' consumers to 'active' prosumers. Renewable Energy Communities (RECs) are envisioned as modes of collective prosumership by citizens under the Renewable Energy Directive of 2018 (RED II). A holistic understanding of RECs is essential to identify the benefits and challenges of collective prosumership. RECs have been the topic of several modelling studies, but a single model that simulates RECs from an integrated perspective—combining technical, economic and ecological analysis—is absent. Wide variability in the indicators discourages comparison of the results across studies. This article builds on the existing knowledge by proposing an integrated model to undertake a multi-disciplinary assessment of a potential REC. First, the proposed model analyses the technical possibilities of collective prosumership using energy flow analysis based on consumption and generation profiles. Second, the model evaluates the economic impacts of prosumership from two perspectives: from the consumers' perspective (in terms of the annual cost of energy consumption) and from an investor's perspective (in terms of the net present value of the investment). Thirdly, the model quantifies the annual greenhouse gas emissions of energy consumption (expressed in CO 2 equivalent) to evaluate the ecological impact of prosumership. Lastly, a set of key performance indicators (KPIs) are proposed that can be used to interpret and compare the results of simulations and are mapped to the actors in the REC in line with their objectives. The proposed approach offers a single, replicable model that can be used to simulate RECs in the different Member States of the European Union. The KPIs can be used to compare the impact of combinations of various prosumership activities within the same REC or to compare two different RECs on the benefits offered vis-a-vis the investments incurred. The KPIs also offer insights into the aligning and conflicting objectives of the stakeholders of the REC. [ABSTRACT FROM AUTHOR]

The transition towards more sustainable energy management can be supported by the diffusion of energy communities, i.e., coalitions of prosumers that are willing to exchange the energy produced locally. The optimization of energy management requires the solution of a prosumer problem that can become impractical when the number of users increases. This paper presents a parallel approach, based on an edge computing architecture, which is suitable for large communities. The users are partitioned into groups whose proportions, in terms of producers and consumers, mirror the composition of the whole community. The prosumer problems for the different groups are first solved separately and in parallel by local edge nodes. Then, the solutions are combined by a central entity to redistribute the energy among the groups and minimize the exchange of energy with the external grid. A set of experiments show that the parallel approach, when compared with an approach that solves the optimization problem in a single stage, leads to a notable reduction of computing resources, and becomes feasible in large communities for which the single-stage approach is impossible. Moreover, the achieved solution is close to the optimal solution in terms of energy costs. [ABSTRACT FROM AUTHOR]

The use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the potential of peer-to-peer energy transactions to promote energy cost reductions and enable the validation of auction-based energy transactions and the use of energy forecasting models in today's buildings and end-users. [ABSTRACT FROM AUTHOR]

This paper presents an analytical framework to develop a hierarchical energy management system (EMS) for energy sharing among neighbouring households in residential microgrids. The houses in residential microgrids are categorized into three different types, traditional, proactive and enthusiastic, based on the inclusion of solar photovoltaic (PV) systems and battery energy storage systems (BESSs). Each of these three houses has an individual EMS, which is defined as the primary EMS. Two other EMSs (secondary and tertiary) are also considered in the proposed hierarchical energy management framework for the purpose of effective energy sharing. The intelligences of each EMS are presented in this paper for the purpose of energy sharing in a residential microgrid along with the priorities. The effectiveness of the proposed hierarchical framework is evaluated on a residential microgrid in Australia. The analytical results clearly reflect that the proposed scheme effectively and efficiently shares the energy among neighbouring houses in a residential microgrid. [ABSTRACT FROM AUTHOR]

To ensure broad application of renewable and energy-efficient energy systems in buildings and neighborhoods, profitable business models are vital. Energy supply contracting helps building residents to overcome the barrier of high upfront investment costs while additionally reducing risks related to energy-saving mechanisms. This study examines profitability for energy contractors in a variety of business cases that simultaneously ensure energy cost savings for the residents. A mixed-integer linear optimization model is developed for a neighborhood energy community, consisting of three buildings with diverse usages. In the process, the optimum capacities of building-attached and building-integrated photovoltaics, a heat pump and a gas-fired mini combined heat and power unit are determined to cover the energy community's electricity and heat load. Results show that cross-domain contracting within energy communities is highly profitable for both, the contractor and the residents, while the extent depends on the accounting method, assumed interest rate and depreciation time. The additional application of energy-efficiency measures in/on the buildings, constituting a combination of energy supply and energy performance contracting, further increases profitability. The investigation of several sensitivities shows that high grid energy costs for electricity and gas enhance profitability of local energy technologies, leading to an increase in optimal technology capacities. [ABSTRACT FROM AUTHOR]

Balancing energy generation and consumption is essential for smoothing the power grids. The mismatch between energy supply and demand would not only increase the cost on both sides, but also has a great impact on the stability of the system. This paper proposes a novel energy sharing mechanism (ESM) to facilitate the consumption of local energy. With the help of the ESM, multiple prosumers have an opportunity to share surplus energy with neighboring prosumers. The problem is formulated as a leader–follower framework based on the Stackelberg game theory. To address the aforementioned problems, a deep deterministic policy gradient (DDPG) is applied to solve the Nash equilibrium (NE). The numerical results demonstrate that the proposed method is more stable than the conventional reinforcement learning (RL) algorithm. Moreover, the proposed method can converge to NE and find a relatively good energy sharing (ES) pricing strategy without knowing the specific system information. In short, it is notable that the proposed ESM can be seen as a win–win strategy for both prosumers and the power system. [ABSTRACT FROM AUTHOR]

In this research, the greenhouse gas (GHG) emission reduction potentials of electric vehicles, heat pumps, photovoltaic (PV) systems and batteries were determined in eight different countries: Austria, Belgium, France, Germany, Italy, the Netherlands, Portugal and Spain. Also, the difference between using prosuming electricity as a community (i.e., energy sharing) and prosuming it as an individual household was calculated. Results show that all investigated technologies have substantial GHG emission reduction potential. A strong moderating factor is the existing electricity generation mix of a country: the GHG emission reduction potential is highest in countries that currently have high hourly emission factors. GHG emission reduction potentials are highest in southern Europe (Portugal, Spain, Italy) and lowest in countries with a high share of nuclear energy (Belgium, France). Hence, from a European GHG emission reduction perspective, it has most impact to install PV in countries that currently have a fossil-fueled electricity mix and/or have high solar irradiation. Lastly, we have seen that energy sharing leads to an increased GHG emission reduction potential in all countries, because it leads to higher PV capacities. [ABSTRACT FROM AUTHOR]

Off-grid solar home systems (SHSs) currently constitute a major source of providing basic electricity needs in un(der)-electrified regions of the world, with around 73 million households having benefited from off-grid solar solutions by 2017. However, in and of itself, state-of-the-art SHSs can only provide electricity access with adequate power supply availability up to tier 2, and to some extent, tier 3 levels of the Multi-tier Framework (MTF) for measuring household electricity access. When considering system metrics of loss of load probability (LLP) and battery size, meeting the electricity needs of tiers 4 and 5 is untenable through SHSs alone. Alternatively, a bottom-up microgrid composed of interconnected SHSs is proposed. Such an approach can enable the so-called climb up the rural electrification ladder. The impact of the microgrid size on the system metrics like LLP and energy deficit is evaluated. Finally, it is found that the interconnected SHS-based microgrid can provide more than 40% and 30% gains in battery sizing for the same LLP level as compared to the standalone SHSs sizes for tiers 4 and 5 of the MTF, respectively, thus quantifying the definite gains of an SHS-based microgrid over standalone SHSs. This study paves the way for visualizing SHS-based rural DC microgrids that can not only enable electricity access to the higher tiers of the MTF with lower battery storage needs but also make use of existing SHS infrastructure, thus enabling a technologically easy climb up the rural electrification ladder. [ABSTRACT FROM AUTHOR]

Smart grids are robust, self-healing networks that allow bidirectional propagation of energy and information within the utility grid. This introduces a new type of energy user who consumes, produces, stores and shares energy with other grid users. Such a user is called a "prosumer." Prosumers' participation in the smart grid is critical for the sustainability and long-term efficiency of the energy sharing process. Thus, prosumer management has attracted increasing attention among researchers in recent years. This paper systematically examines the literature on prosumer community based smart grid by reviewing relevant literature published from 2009 to 2018 in reputed energy and technology journals. We specifically focus on two dimensions namely prosumer community groups and prosumer relationships. Based on the evaluated literature, we present eight propositions and thoroughly describe several future research directions. [ABSTRACT FROM AUTHOR]

With the development of renewable energy technology and communication technology in recent years, many residents now utilize renewable energy devices in their residences with energy storage systems. We have full confidence in the promising prospects of sharing idle energy with others in a community. However, it is a great challenge to share residents’ energy with others in a community to minimize the total cost of all residents. In this paper, we study the problem of energy management and task scheduling for a community with renewable energy and residential cogeneration, such as residential combined heat and power system (resCHP) to pay the least electricity bill. We take elastic and inelastic load demands into account which are delay intolerant and delay tolerant tasks in the community. The minimum cost problem of a non-cooperative community is extracted into a random non-convex optimization problem with some physical constraints. Our objective is to minimize the time-average cost for each resident in the community, including the cost of the external grid and natural gas. The Lyapunov optimization theory and a primal-dual gradient method are adopted to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on State-action-reward-state-action (Sarsa) Algorithm, in the condition that each residence in the community is able to communicate with its neighbors by a central controller. Finally, extensive simulations are presented to validate the proposed algorithms by using practical data. [ABSTRACT FROM AUTHOR]