Community Energy Systems Research Articles

Cooperative V2G-enabled vehicle-to-vehicle sharing in energy and reserve markets: A coalitional approach

The dynamics of electric vehicles (EVs) charging significantly influence the current power system dynamics. However, with advancements in battery technology and charging infrastructure, EVs can also serve as energy storage systems through vehicle-to-grid (V2G) technology. This opens up possibilities for novel approaches, such as a coalition-based V2G-enabled vehicle-to-vehicle (V2V) energy and reserve sharing mechanism. Unlike traditional transactive energy models that often under-utilize EVs due to mismatches with smaller renewable outputs and peak loads, the proposed cooperative V2V sharing mechanism aims to maximize the use of EVs’ charging and discharging capabilities. It forms a grand coalition of EV users to optimize energy and reserve market participation. The model introduces mathematical formulations to describe how EVs collaborate in both energy and reserve markets. It ensures fairness and stability in pay allocations among users within the cooperative framework. The theoretical foundation includes proof of balance in the coalition approach and a two-stage imputation method to achieve fair and optimal payoff distribution. This minimizes incentives for users to defect from the coalition, ensuring stability. To address scalability challenges inherent in coalition formation problems, a decomposition algorithm is proposed. This algorithm enhances efficiency in solving problems that grow exponentially with the number of users. The effectiveness and superiority of this approach are validated through applications to various community energy systems of different sizes. The proposed plan can increase 22.21% of the total payoff in 10-users case and 22.39% in 30-users case. The computation time scales near-linearly with the number of users, although the computation scales exponentially with it. These demonstrations highlight its capability in modeling and solving complex energy sharing scenarios.

Optimized scheduling of integrated community energy stations based on improved NSGA-III algorithm

To cope with the problem of building and using a large number of fast electric vehicle stations (FEVS), adding FEVS to the integrated community energy system (ICES) provides new ideas for solving the problems of interaction with the grid, overall system stability, revenue benefits, and environmental pollution. To this end, this paper designs an ICES model with FEVS (ICES-FEVS). This model considers economy, stability, and sustainability to achieve all-around consideration. To further solve the complex multi-objective problem, the multi-objective improvement strategy NSGA-III algorithm (IS-NSGA-III), combined with fuzzy comprehensive evaluation (FCE), is used to solve the model. In the solution phase, the advantages of the IS-NSGA-III algorithm are first verified by the benchmark function, and the validity of the ICES-FEVS model is verified by comparing different models. In the optimization results, daily revenue was increased by 6.3 %, daily battery life loss was increased by 26.03 %, and pollution emissions were reduced by 46.9 %. The analysis of the results shows that introducing Electric Vehicle (EV) fast charging stations in an integrated community energy system ensures economic improvement, sustainable development, and more favorable regulation of the energy storage system. The simulation results show that compared with other algorithms, the HV metrics of IS-NSGA-III are lower by about 6.8 %, Spread metrics are better by about 1.35 %, and the proposed IS-NSGA-III is better in terms of convergence and distributivity.

How urban–rural differences and ageing affect residents’ occupancy patterns – An empirical study based on China national time use survey

Occupancy behavior plays a crucial role in residential community planning, building design, and energy system optimization for sustainable development, particularly in the face of climate change. Research on residential occupancy behavior often lacks comprehensive empirical data and fails to account for the diversity of influencing factors such as demographic features and urban–rural distinctions. This study utilizes a national time-use survey to examine occupancy patterns in urban and rural households, aiming to identify key factors influencing these patterns. A K-shaped cluster analysis was applied to extract typical occupancy patterns for weekdays and weekends, with a subsequent comparison between urban and rural data. The study also analyzed the impact of demographic and social factors on total hours spent at home. The results indicate that urban residents tend to concentrate their occupancy on weekdays, while weekends show more dispersed patterns. Furthermore, time spent at home by urban residents is positively correlated with the age of family members and negatively correlated with working status and education level. Given the aging demographic, older urban families are likely to spend more time at home on weekdays. These findings contribute to a better understanding of residential occupancy behavior in China.

Developing community energy systems to facilitate Ethiopia's transition to sustainable energy

In Ethiopia, renewable energy offers people an affordable, dependable, and eco-friendly power supply while decreasing the carbon footprint. However, delivering a renewable future for the country requires a massive change in social practices and systems of provision. The slow progress of renewable development is hindering the transition to a cleaner energy future. Over 80 % of people live in rural areas where it is expensive to reach them via grid networks in Ethiopia, requiring off-grid alternatives.Community energy systems, which are off-grid energy systems in which communities play a key role, offer alternative strategies to close the country's energy access gap. However, community energy systems remain underdeveloped in Ethiopia. There is a need to understand the opportunities for community energy and the barriers that hinder its development in Ethiopia, and their role in energy transitions.This paper adopts an experimental lens to understand the diverse dimensions of community energy projects through how they are made, maintained, and lived. Using a comparative analysis of three multi-method, qualitative case studies, this paper argues that the political context poses the biggest obstacle to the development of community energy in Ethiopia despite these projects' tangible benefits.The analysis indicates that community energy projects allow communities to be involved in all stages of project development. In every project, communities assume project management responsibilities after commissioning. However, these projects encounter challenges in resourcing capital, managing supply chains, and building necessary skills among community members to understand business models to ensure sustained operation of the systems.

UHVDC transmission line diagnosis method for integrated community energy system based on wavelet analysis

With the large-scale development of renewable energy power, China has faced with the challenges of the reverse regional distribution of wind and solar resources and power load, as well as the intermittency and randomness of renewable energy power. Therefore, China is vigorously developing ultra-high voltage direct current (UHVDC) transmission technology to solve the problem of insufficient flexibility caused by the uncertainty of renewable energy and the fluctuation of multi-energy loads in integrated community energy systems. UHVDC plays an increasingly pivotal role in the west-east transmission system in China’s power system due to its high transmission capacity and long transmission distance. Once the fault occurs in the ultra-high voltage direct (UHVD) transmission line, quick and accurate fault location identification is of great significance. Hence, this paper proposes a UHVDC transmission line diagnosis method based on wavelet analysis for integrated community energy systems. Wavelet transform (WT) is used to decompose the transient signal on a multi-scale, and then power systems computer-aided design (PSCAD) software is utilized for simulation calculation to obtain the singular spectrum entropy of each layer and facilitate wavelet transformations for signal denoising with advanced tools such as MATLAB. The prediction results can distinguish outside the rectification side fault, within the rectification side fault, and outside the inverter fault with an accuracy of 100%. A large number of simulations demonstrate that combining singular spectrum entropy with support vector machines (SVM) has emerged as a robust technique for integrated community energy systems, suggesting its potential as a standard method in UHVDC transmission line diagnosis. This study is of significant reference for realizing the complementarity of multiple types of power supply and ensuring a reliable power supply.

Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach

Community energy systems, integrating electricity storage, smart transportation, and flexible energy interactions can mitigate renewable energy intermittency and uncertainty, and stabilize local grids. The battery circular economy, involving cascade use, reuse and recycling, aims to reduce energy storage costs and associated carbon emissions. However, developing multi-scale and cross-scale models based on physical mechanisms faces challenges due to insufficient expertise and temporal discrepancies among subsystems. This study proposes a general machine learning approach for a source-grid-demand-storage community with a battery circular economy, streamlining data preparation, model training, validation, and testing. Machine learning models replace traditional physical models for energy consumption, renewable energy supply, and battery cycling ageing, improving computational efficiency. Furthermore, a battery circular economy principle is proposed, which suggests reusing 80 % capacity retired electric vehicle batteries in buildings and recycling at 60 % capacity. The study validates machine learning-based surrogate models against traditional physical models. Results show an 82.5 % reduction in computational time, from 40 to 7 min without significant deviation in techno-economic-environmental performance (relative difference ≤3 %). This study offers a framework for machine learning in the 'source-grid-demand-storage' system within a battery circular economy, enhancing computation efficiency and accuracy for low-carbon transitions and informing multi-scale, cross-scale model advancement.

Optimal pricing of integrated community energy system for building prosumers with P2P multi-energy trading

Buildings are typically integrated with multiple distributed energy resources (DERs), enabling them to act as building prosumers engaged in both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers is crucial to improve their benefits. However, further exploration is required to balance the benefits between building prosumers and the system operator (e.g., the integrated community energy system (ICES) operator) since they are different entities. In this context, this paper proposes a comprehensive network charge and energy sale pricing scheme for the ICES operator on heterogeneous building prosumers with P2P multi-energy trading. The interaction between the ICES operator and building prosumers is modelled as a bi-level optimization problem that belongs to the hierarchical structure, while considering the heterogeneity of thermal insulation performance of buildings. At the upper level, the ICES operator optimizes the electricity/heat network charge prices and electricity/heat sale prices to maximize its revenue. At the lower level, building prosumers with a parallel structure optimize the schedules including P2P multi-energy trading and buildings' heating loads to minimize their costs. Furthermore, to address the bi-level optimization problem with parallel and hierarchical coupling structures, an accelerated asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed, integrating a warm start strategy and a dual update accelerated iteration strategy for further improving computational efficiency. Finally, case studies demonstrate that the proposed scheme can effectively benefit both the ICES operator and building prosumers in P2P multi-energy trading at the same time. Meanwhile, the feasibility and effectiveness of the proposed algorithm are validated.

Matchmaking in Off-Grid Energy System Planning: A Novel Approach for Integrating Residential Electricity Demands and Productive Use of Electricity

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.

Optimised operation of integrated community energy system considering integrated energy pricing strategy: A two-layer Stackelberg game approach

Aiming at the problems of new energy absorption and inter-subject interest conflict in the integrated community energy system, this study proposed an integrated energy pricing strategy that considered the interconversion of various energy sources and built a two-layer optimisation model of the electric-gas-heat‑hydrogen interconnection integrated community energy system. The upper-layer operator model maximises the revenue by setting energy price and selling energy to users. The lower-layer users model minimises the energy cost by actively adjusting energy usage strategy according to energy price. The two sides rationally pursue the maximisation of their respective interests, and the process of bargaining can be described by Stackelberg game. The model is also considered for combining the characteristics of elastic load and vehicle-to-grid on the demand side and operates jointly with the bus battery swapping station and two-stage power-to-gas for optimizing system operation. Through theoretical derivation, it is proved that there is a unique Stackelberg equilibrium solution for the proposed game model. Further, the two-layer optimisation model is transformed into a mixed integer quadratic programming problem by applying the Karush-Kuhn-Tucher optimal condition, linear relaxation technique, and duality theorem. Finally, the global optimal energy price is obtained by solving the converted model. The optimisation results in different scenarios show that the proposed model and method not only protect the interests of the operator and users but also improve the wind power absorption capacity of the community and reduce the load fluctuation.

Optimized scheduling of smart community energy systems considering demand response and shared energy storage

Integrated energy systems within communities play a pivotal role in addressing the diverse energy requirements of the system, emerging as a central focus in contemporary research. This paper contributes to exploring optimal scheduling in a smart community featuring multiple smart buildings equipped with a substantial share of distributed photovoltaic sources, shared energy storage, and controllable loads. The study formulates a two-stage scheduling optimization model incorporating multiple stakeholders, explicitly examining the game dynamics between the smart community operator and the customer load aggregator. The weights assigned to each optimization objective are determined using an analytic hierarchy process-entropy weight method to establish a balanced and nuanced approach. The ensuing optimal scheduling encompasses unit output and energy trading considerations, focusing on enhancing the system's economic viability, safety measures, and environmental sustainability. A comprehensive evaluation is conducted to validate the proposed model by comparing its performance with alternative operational strategies. The results show that the designed strategy can reduce the operating cost by 40.22% and increase the level of PV consumption by 22.57% compared to the traditional heat-based strategy, which is effective in mitigating power fluctuations, smartly responding to dispatch peak demand, enhancing new energy integration, and ensuring the security of grid operation.

Community resilience in Bondo community, Southern Malawi: balancing energy, water and biodiversity

Purpose This study aims to investigate the intricate relationships between a community energy system, water resources and biodiversity conservation, with a specific focus on augmenting community energy resilience in Bondo. The primary objective is to gain an in-depth understanding of how community members perceive and experience the challenges related to balancing the often-conflicting demands of energy, water and biodiversity conservation within this context. Design/methodology/approach The research uses a qualitative approach to unravel the multifaceted dynamics of community energy systems, water resources and biodiversity conservation in Bondo. Data were collected through focus groups and direct observations, enabling a nuanced exploration of community perspectives and lived experiences. The subsequent analysis of this qualitative data follows established thematic analysis procedures. Findings The study's findings shed light on the formidable barriers that impede rural communities in Malawi from accessing electricity effectively. Even in communities fortunate enough to have electricity connections, the lack of knowledge regarding productive electricity use results in community energy systems operating at significantly reduced load factors. Furthermore, the intricate challenge of managing a biodiversity hotspot persists, exacerbated by the densely populated peripheral communities' continued reliance on forest, land and water resources. These activities, in turn, contribute to ecosystem degradation. Originality/value In a context where government-led management of forest reserves and game reserves has not yielded the expected results due to a multitude of factors, there arises a compelling need for innovative approaches. One such innovation involves fostering partnerships between the government and experienced trusts as lead organisations, providing a fresh perspective on addressing the complex interplay between community energy systems, water resources and biodiversity conservation. This novel approach opens doors to explore alternative pathways for achieving the delicate balance between human energy needs and the preservation of vital ecosystems.

Multi-time scale operation optimization for a near-zero energy community energy system combined with electricity-heat-hydrogen storage

Hybrid energy storage offers a new way to increase the renewable energy proportion (REP) of near-zero energy community energy system (NZECES). However, the optimization operation method of NZECES combining hybrid energy storage is not yet mature. Therefore, this paper proposes integrating electricity storage, heat storage and hydrogen storage into the NZECES, and conducting multi-time scale (MTS) operation optimization research. Firstly, a NZECES with electricity-heat-hydrogen storage is constructed. Secondly, a MTS optimization operation model of day-ahead scheduling, intra-day rolling, and real-time adjustment is established. Among them, the hourly scheduling scheme targeting the lowest daily operating cost is obtained through the optimization of the day-ahead stage. In the intra-day stage, the scheduling scheme with a time interval of 15 min is obtained by the rolling optimization. The operation scheme of electrical-related equipment with the minimum adjustment cost is obtained in the real-time stage. Finally, a NZEC with different building types is used as the energy supply object. The results show that compared to the traditional day-ahead scheduling scheme, the REP is increased by 3.6 % and the operation cost of the energy system is reduced by 5.3 % by using the MTS operation optimization method.

Trust & Fair Resource Allocation in Community Energy Systems

Trust & Fair Resource Allocation in Community Energy Systems

Multi-Objective Operation Optimization Strategy for Integrated Community Energy Systems Considering Demand Side Management

Multi-Objective Operation Optimization Strategy for Integrated Community Energy Systems Considering Demand Side Management

Energy transition in a developing economy: Challenges, prospects and policy considerations

Energy transition as a process of decarbonizing energy systems is considered a key mitigation strategy in the global effort to curb rising temperatures stemming from greenhouse gases. The main challenge with the energy transition process has to do with the social, economic, political and technological capacities of a country to undertake this transition process for which developing countries globally are lagging behind. The purpose of this study is the present a policy discussion paper looking at the pertinent social, economic and institutional challenges that developing countries need to consider in its energy transition process. As a case study, the paper also looked at the economy of Guyana, a country that is experiencing an oil and gas boom and also has committed to developing a low carbon development strategy. The paper implemented a narrative literature review and constructed the case study for Guyana utilizing secondary information on policy positions and directives for the energy sector in Guyana. Several pertinent issues within developing countries were identified including centralized and state dominated markets, poor infrastructure to support the transition process, availability of technology, energy justice and energy bullying and a lack of policies that foster community energy systems, energy sensitization and energy investments. These issues also reverberated with Guyana with policy recommendations emphasizing on collective actions needed to address the policy needs of developing countries in their transition process. The issues identified within the policy discussion of this paper can be used by policy makers globally as a potential framework for identifying the research gaps and priority areas to channel resources and technical skills to enhance the energy transition process in developing countries.

Exergy-based ecological network analysis for building and community energy systems

Although buildings are transitioning towards complex, dynamic, and interconnected systems, traditional engineering metrics that dominate today don't capture several important whole-network properties. To address this, this paper adopts ecological network analysis (ENA), which has numerous successes for natural ecosystems and socio-technical systems but has yet to be applied for buildings. After translating ENA into comprehensive mathematical models for engineering applications, the novel ENA method is demonstrated with building and community energy systems. For the models to suit building energy systems, which have multiple energy types and non-negligible dynamics, ENA is formulated on an exergy basis, with dynamic flows and balances (i.e., time-varying storages). To demonstrate the proposed approach, ENA is used to redesign the heating and cooling systems for an office building and data center, coupling the buildings together via ambient-loop district energy. The models are implemented using the equation-based Modelica language. Results indicate that the ENA-guided redesign reduces the source energy by 15%. The energy consumed by the heating and cooling systems is reduced by 84% with negligible sacrifice to the thermal performance. Surprisingly, the redesign also reduced the exergy efficiency of the total system from 60% to 34% due to a greater decrease in exergy output relative to exergy input with low-exergy system designs. This indicates that ENA and other network approaches that classify system organization may outperform traditional efficiency-based metrics for building and community energy systems when whole-system perspectives are desired.

The role of community energy systems to facilitate energy transitions in Ethiopia and Mozambique

AbstractPolicymakers and academics are focusing on energy transition to provide affordable, sustainable, and green energy for everyone. This is being driven by a combination of the lack of electricity access to millions of people particularly in the African continent and the requirement for the reduction of environmental impact through the use of greener energy resources and systems. This paper summarizes an interdisciplinary research program investigating community energy systems in Ethiopia and Mozambique to facilitate energy transitions. Specifically, it compares community energy landscapes, progress made, and existing challenges and opportunities. To determine the status of community energy development in the two countries, recent publications and official policies were reviewed, and community energy managers were interviewed. The review showed that renewable energy sources are the dominant focus for community energy developments, which is key to achieving a cleaner energy future. However, progress in community energy development has been slow in these countries. There are several reasons that hinder community energy systems from driving the necessary energy transition to a cleaner, modern, and affordable energy. Some of these reasons are the absence of favorable regulatory frameworks, incentive package, knowledge on business models, weak commitments from stakeholders, and insufficient community involvement. These issues vary in degree between the two countries.

Coordinated design of multi-stakeholder community energy systems and shared energy storage under uncertain supply and demand: A game theoretical approach

Shared energy storage plays an important role in achieving sustainable development of renewable-based community energy systems. In practice, the independent or disordered planning of community energy systems and shared storage systems can lead to suboptimal design without considering the complex interactions between neighboring energy systems. Therefore, a coordinated design approach for community energy systems and shared energy storage is proposed, and a pricing mechanism for storage sharing based on bounded rationality theory is developed. A Stackelberg game is introduced to enable consideration of storage sharing among energy systems at the design phase. The uncertainties of solar irradiation, wind speed, and electrical demand on both the supply and demand sides are also considered. Then, an uncertainty model is formed to optimize the system configurations, operation strategies, and storage sharing strategies of each stakeholder, simultaneously. Furthermore, the influence of the profit margin, storage investment cost, and carbon tax on the storage rental price and system design strategies are discussed. Illustrative examples highlight the feasibility and applicability of the shared storage pricing mechanism and coordinated design approach. This paper provides references for the government with respect to the problem of coordinated design for multi-stakeholder distributed energy systems and storages in a market-oriented environment.

Environmental impacts of optimal designs of community energy systems under different CO2 footprints of electric grids

This work determines optimal energy system configurations that minimize CO2 emissions under three typical grid electricity generation sources: (a) mostly nuclear and hydro; (b) natural gas; (c) coal. Benefits of implementing ground source heat pump (GSHP) and air source heat pump (ASHP) are compared to using combined cooling-heating-power (CCHP) system considering meeting cooling, heating, and electricity demands of consumers. GSHP is modeled via a novel multilayer model of borehole field heat transfer. Results from case studies show that for regions using mainly renewable energy technologies, configuration anchored on GSHP + ASHP has the lowest emissions, while configurations anchored on ASHP or ASHP + CCHP emit 5% more. When electricity is generated from natural gas or coal, relative to the configuration with the lowest emissions (all equipment), configurations anchored on GSHP + CCHP emits 1% and 2% more, while configurations anchored on ASHP + CCHP emit 3% and 19% more, respectively.

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.