Self-consumption Of Renewable Energy Research Articles

Study on distributed renewable energy generation aggregation application based on energy storage

As large amounts of distributed renewable energy generation (DREG) replace conventional generating units on the grid, the tension between the supply lack of flexible resources and the increased demand for flexible resources on the grid intensifies. To address this issue, this paper focuses on distributed renewable energy generation aggregation (DREGA) applications based on energy storage systems (ESS). Considering the interconnection and supply–demand coupling between generation and consumption in the grid, ESS is used to flexibly regulate the flow of electrical energy between producers and consumers in DREGA. A pricing methodology that takes into account aggregation differences and real-time changes based on supply and demand is developed. This method mobilizes resources within the aggregator to help the network operator balance supply and demand through price incentives. Electricity is shared directly between producers and consumers within the aggregation, in addition to indirectly shared through ESS. It improves the self-consumption of renewable energy and the self-supply rate of users from the perspective of aggregators. The validity of the proposed method is proven by case study results.

Development of an Energy Community through semi-dynamic simulation of a urban social housing

Energy communities (ECs) are instrumental in driving the transition to renewable energy in urban areas. This study focuses on implementing ECs in Rome’s Tor Bella Monaca district, with a particular focus on linear mass housing. Using the Urban Modeling Interface (UMI) and Simulink, four energy community scenarios were simulated to evaluate their potential outcomes. The scenarios involved integrating photovoltaics and heat pumps into the community’s infrastructure to assess their impact on renewable energy production and CO2 reduction. The results demonstrate that higher electrification within an energy community leads to increased self-consumption of renewable energy and reduced reliance on the grid. Furthermore, the integration of heat pumps enhances energy consumption efficiency. This research highlights the significant potential of energy communities and innovative technologies in managing local renewable energy resources effectively. It provides valuable insights for developing sustainable energy models in urban areas. It emphasizes the importance of carefully evaluating technology sizing, integration, and the inclusion of thermal and electrical storage to maximize self-consumption while minimizing CO2 emissions. The study’s findings offer practical guidance for policymakers, urban planners, and stakeholders involved in sustainable energy management. They underscore the need for a holistic approach that combines technological advancements, community engagement, and thoughtful integration of renewable energy systems. Ultimately, this research contributes to the adoption of energy communities as crucial elements of a resilient and environmentally-friendly future.

Solar parking lot management: An IoT platform for smart charging EV fleets, using real-time data and production forecasts

The fast transition to the electrification of the energy system, combined with an exponential growth of the market share of electric vehicles, is leading to a tight interrelation between electric energy production and transportation, two prominent sectors in fossil fuels consumption and greenhouse gas emissions. Accelerating this process, the management of electric fluxes, aiming at optimizing production and demand coupling, plays a crucial role in reaching the net-zero emission target. The proposed software platform is designed to optimally manage the energy fluxes for a solar powered parking lot, serving a fleet of electric vehicles; the real-time knowledge of energy production and demand, in conjunction with forecasted power generation, allows the maximization of renewable energy self-consumption, thus reducing the exchange with the external grid. The software platform can work either in design mode, allowing the dimensioning of the various parking lot components, or in real-time mode managing instantaneously the energy balance. As a case study, it is tested on the 2019 parking lot mobility data of a research center, assuming a complete transformation of the then existing fleet of employees' cars to electric vehicles. A comparison of the resulting energy flows with those projected by an established commercial tool is performed, as well as a preliminary economic evaluation. Both consistency of the simulation results and favorable economics validate the presented smart charging algorithm and Internet of Things platform for the real-time energy management of a solar parking lot.

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

As an energy-intensive industry, the steel industry grapples with increasing energy costs and decarbonisation pressures. Therefore, multi-objective optimisation is widely applied in the production scheduling of the steelmaking plant. However, the optimal solution prioritising energy savings and emission reductions may lead to impractical or less economically efficient solutions, since the processing time requirement (PTR) of steel production orders in real-world production is neglected. This study fills the research gap by discussing the impact of PTR on the make-span of the steelmaking process and incorporating it into the optimisation model. Considering the variability of PTR, the solving of the multi-objective scheduling problem is transformed into the selection from Pareto solutions with different make-spans. To better leverage the temporal flexibility of the steelmaking process, a what-if-analysis-based strategy coupled with the Normal Boundary Intersection method is proposed to generate a series of evenly distributed Pareto solutions. The energy storage system is integrated to improve the time granularity of the steelmaking plant's flexibility. Our case studies demonstrate that the electricity and emission costs are reduced by 68.5%, indirect emissions are reduced by 83.5%, and the on-site renewable energy self-consumption rate increases by 12.1%. The effectiveness of the proposed method implies that it is of great relevance to the development of a cleaner steel industry in the future.

The role of energy communities in electricity grid balancing: A flexible tool for smart grid power distribution optimization

The unpredictability of renewable energy systems can affect the stability of the electricity grid, causing voltage and frequency imbalances. In this work, a suitable methodology based on the peer-to-peer scheme applied to energy communities is developed and implemented in a simulation tool useful for investigating energy management strategies for decision-making aims. The developed model discretizes the energy community and its users into multiple control volumes, taking into account various technologies. It incorporates energy balances for individual users as well as the entire energy community, considering prosumers, consumers, energy storage systems, and electric vehicles. Moreover, the model enables the exploration of different solutions for grid frequency regulation and optimization of distributed energy resources. Additionally, the tool can predict electricity demand one day ahead, facilitating the organization of renewable energy availability and storage systems to minimize grid interactions and flatten electricity demand. The model incorporates different objective functions, including self-consumption, self-sufficiency, and grid-balancing factors, to evaluate the performance of energy communities. To show the capability of the developed model, it will be adopted to optimize the performance of an investigated community. As a result, an increase in renewable energy self-consumption from 59.4 to 83.9 MW h/year is achieved. Furthermore, the objective of grid balancing was achieved by guaranteeing a non-fluctuating load and providing 1.46 and 7.71 MW h/year for upward and downward grid frequency regulation. These findings illustrate the positive impact of energy dispatching management on the integration of renewable energy sources and the importance of further studying this topic to ensure grid stability.

Green Planning Systems for Self-Consumption of Renewable Energy

Rising temperatures on Earth have led to the exploration of new technologies to help curb the severe effects of climate change. Green planning systems belong to a new type of Internet-based technology that aim to use computing resources efficiently to reduce energy consumption and CO2 emissions. The rapid advancement of renewable energy generation infrastructure and the lack of energy storage solutions at scale have highlighted the importance of energy self-consumption principles in new domains (e.g., heating/cooling, electromobility, and appliances), which refer to the process of intelligently consuming energy at the time it is available. This article analyzes existing green planning systems, energy management applications, and future challenges deploying self-consumption of renewable energy. We also present experiences of a green planning system we developed in-house. Our overview and categorization will be helpful to a variety of researchers, practitioners, and policy makers to make their systems environmentally friendly.

Optimizing hybrid power system at highest sustainability

Abstract The current research investigated the utilization of fuel cells as an energy storage unit in microgrid energy system applications in an effort to enhance self-consumption of renewable energy. The prototype evaluation is constructed of solar photovoltaic and fuel cell energy storage units. The study utilizes experimental weather and electrical load data for household obtained at 1-min temporal resolution. The daily average energy consumption for the evaluated household was 10.3 kWh, with a peak power output of 5.4 kW and an annual energy consumption of 3757 kWh. The Solar System under investigation has a capacity of 3.6 kWp, while the fuel cell system has a capacity of 0–3 kW, allowing for effective integration with the photovoltaic system and a maximum renewable energy fraction. The research indicates that by installing fuel cells powered by hydrogen generated from renewable energy sources, self-consumption and self-sufficiency significantly increase. The annual energy flow demonstrated that the implementation of 2.5 kW fuel cells improved renewable fraction utilization from 0.622 to 0.918, while increasing energy self-consumption by 98.4% to 3338.2 kWh/year and self-sufficiency by 94.41% to 3218.8 kWh/year.

Net-zero energy management and optimization of commercial building sectors with hybrid renewable energy systems integrated with energy storage of pumped hydro and hydrogen taxis

This study develops net-zero energy management and optimization approaches for the commercial building sector in cities powered by renewable energy systems integrated with energy storage of pumped hydro and hydrogen taxis, based on the estimated installation potential of solar photovoltaics and offshore wind power. A novel time-of-use energy management strategy of hybrid renewable energy and storage systems is proposed considering the future-oriented application scenario with high renewable energy generation and shifted peak power periods. Multi-objective optimizations are conducted to find the techno-economic-environmental optimum sizing configurations of the pumped hydro storage. Research results indicate that most of Pareto optimal solutions are achieved with large pumped hydro sizes applied in net-zero energy commercial building sectors. The proposed future-oriented time-of-use strategy significantly improves the system supply and economic performance, in terms of the renewable energy self-consumption (+9.24%), load coverage (+6.10%) and annual electricity bill (-25.21%) compared with the current time-of-use case. Obvious decarbonisation benefits of the renewable power supply and renewable hydrogen mobility are achieved with carbon emission reduction of 95.14% (14713.49 M kg CO2) compared with the traditional grid power supply and 80.74% (293.91 M kg CO2) compared with the traditional petroleum taxis under the future-oriented time-of-use strategy. The proposed net-zero energy planning framework for urban building sectors integrating decarbonized transportation can be applied in similar coastline cities in the globe as important references and guidelines to policymakers for achieving carbon neutrality by 2050.

Optimizing a microgrid photovoltaic-fuel cell energy system at the highest renewable fraction

The current research examined the usage of fuel cells as an energy storage unit to increase renewable energy self-consumption in microgrid energy system applications. The studied model is comprised of photovoltaic modules and a fuel cell that serves as the energy storage unit. The study was conducted in 2020, utilizing real-time weather and electrical load data with a one-minute temporal resolution. The daily average energy consumption for the analyzed household was 10.1 kWh, with a peak power output of 5.3 kW, and the yearly energy consumption was 3755 kWh. The investigated photovoltaic system has a capacity of 2.7 kWp (6 modules at 0.45 kWp/module), and the fuel cell capacity is in the range of 0–3 kW in order to obtain optimal integration with the photovoltaic system to get maximum renewable energy fraction utilization. The findings indicate that using fuel cells powered by hydrogen generated by renewable energy systems can significantly increase self-consumption and self-sufficiency. The annual results showed that the use of 2.5 kW fuel cells can increase renewable fraction utilization from 0.622 to 0.918 with a 2.5 kW fuel cell, and energy self-consumption can reach 3338.2 kWh/year, an increase of 98.4%, and energy self-sufficiency can reach 3218.8 kWh/year, an increase of 94.41%. The results obtained demonstrate that the proposed photovoltaic fuel cell energy system provides a viable option to run semi-autonomous or fully autonomous applications in a self-sustaining medium at a percentage of 95%. Furthermore, the economic aspect is analysed for the optimal system configuration. • For greenhouse applications, a hybrid energy system with a robust storage unit is used. • Optimizing the storage system capacity at the highest renewable fraction. • In conjunction, fuel cells with photovoltaics can improve energy self-consumption and self-sufficiency. • Shared photovoltaic energy generation with fuel cell energy can add customer value to the improvement of the grid. • The levelized cost of energy was calculated to be about $0.23/kWh for the proposed system.

Electrical demand aggregation effects on the performance of deep learning-based short-term load forecasting of a residential building

Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply. Hence, increasing the self-consumption of renewable energy through demand response in households, local communities, and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance. Although many of the recent studies have investigated both macro and micro scale short-term load forecasting (STLF), a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal, especially with large sample sizes, where it is essential for optimal sizing of residential micro-grids, demand response markets, and virtual power plants. Hence, this study comprehensively investigates STLF of five aggregation levels (3, 10, 30, 100, and 479) based on a dataset of 479 residential dwellings in Osaka, Japan, with a sample size of (159, 47, 15, 4, and 1) per level, respectively, and investigates the underlying challenges in lower aggregation forecasting. Five deep learning (DL) methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping, where a detailed comparative analysis is developed. The test results reveal that a MAPE of (2.47–3.31%) close to country levels can be achieved on the highest aggregation, and below 10% can be sustained at 30 aggregated dwellings. Furthermore, the deep neural network (DNN) achieved the highest performance, followed by the Bi-directional Gated recurrent unit with fully connected layers (Bi-GRU-FCL), which had close to 15% faster training time and 40% fewer learnable parameters.

스마트 팜의 재생에너지 자가 소비 촉진을 위한 최적 포트폴리오 분석

The purpose of this study is to analyze the ways to expand the supply of renewable energy to large-scale facility farms and to derive policies to promote self-consumption of renewable energy for efficient use and operation of energy nationwide. This is ultimately intended to reduce power consumption for agriculture supplied from fossil fuel power plants. We assume three main technologies such as photovoltaic power generation, energy storage system(ESS), and air heat pump for smart-farm models, and the objective function and conditions for maximizing cost-benefit are analyzed through linear programming. Assuming several scenarios reflecting various conditions of REC price, CO₂ emission price, and agricultural electricity rate, which are the benefit factors of operators who installs renewable energy, we present some conditions for promoting self-consumption of renewable energy and emission price levels through simulation. The results of this study can contribute to increase the benefits of smart farm models with renewable energy installed and the efficiency of nationwide power system management.

Assessing the Scalability and Privacy of Energy Communities by Using a Large-Scale Distributed and Parallel Real-Time Optimization

In the context of the energy transition, energy communities are gaining increasing attention all over the world, in recent years. By participating in an energy community, prosumers may take a leading role in the energy transition and improve the self-consumption of renewable energy produced inside the community. Prosumers can carry out energy exchanges inside the energy community and provide ancillary services to the system operators, thus contributing to improve the efficiency and stability of the grid. A novel scalable, privacy-preserving, and real-time distributed parallel optimization is proposed to manage a large-scale energy community, considering energy exchanges inside the community according to the model of virtual self-consumption and the provision of ancillary services. The proposed method preserves the privacy of prosumers and allows the assessment of the impact of energy exchanges on the ancillary services provided by an energy community. Simulation results confirmed that the proposed method is superior in terms of privacy if compared with the equivalent centralized optimization and that it has a convergence rate higher than that of the splitting conic solver (SCS).

SCORES "Self-Consumption Of Renewable Energy by hybrid Storage systems"

SCORES "Self-Consumption Of Renewable Energy by hybrid Storage systems"

Towards Net Zero Energy Factory: A multi-objective approach to optimally size and operate industrial flexibility solutions

This study proposes a methodology for sizing and operating new flexibility options within a German carpentry, targeting to be operated as Net Zero Energy Factory (NZEF). A key element of this system is the maximization of the integration of the electric power locally generated by a photovoltaic plant and the electric demand for driving the manufacturing processes. This aim is achieved with a proper integration between design choices in terms flexibility options and optimal control of energy fluxes. In this work, benefits and criticalities arising from the integration of different flexibility options, such as stationary and mobile Energy Storage Systems, are identified and analyzed.A double step optimization process is implemented. First, a Model Predictive Control strategy is used to schedule the manufacturing machines and the energy storage systems (stationary and mobile). Then, a multi-objective optimization aiming at the minimization of annual energy grid exchange and the optimal exploitation of battery capacity is carried out with the Genetic Algorithm. Such a methodology allows the factory operators to optimally size the flexibility capacity (the battery energy storage in this application) needed to operate their industrial facility as a net-zero energy factory.Results show that an optimally controlled stationary energy storage system allows a reduction of energy exchange with the grid up to 53%. The further introduction of electric vehicles increases of about 5% and 67% the renewable energy self-consumption and carbon emissions savings, respectively, ensuring also a significant increase in the yearly annual savings (up to 406%).

Are We on the Right Track? Collective Self-Consumption and Energy Communities in the European Union

To accelerate the energy transition, the EU “Clean Energy for all Europeans” package aims to strengthen the involvement of end consumers in the energy market. To this end, together with so-called “active consumers” and provisions for individual and collective renewable energy self-consumption, two types of energy communities were introduced. The EU framework, however, leaves many details of the transposition process to the national level. The corresponding directives were supposed to be transposed by the end of December 2020 (recast Electricity Market Directive, defining active consumers and citizen energy communities) and by the end of June 2021 (Renewable Energy Directive, defining renewables self-consumption and renewable energy communities). In this paper, we critically discuss major developments of the transposition, including questions of the general distinction of the different concepts, governance and ownership, physical expansion, administrative barriers and the overall integration of energy communities into the energy system. The analysis builds on country case studies as well as on previous work by the authors on the status of the transposition process throughout the EU. The paper shows that the national approaches differ greatly and are at very different stages. While basic provisions are in place in most Member States to meet the fundamental EU requirements, the overall integration into the energy system and market is only partly addressed. This concerns, for instance, the analysis of system impacts of energy communities and measures that would allow and support energy system-friendly behaviour. In addition, several practical hurdles need to be overcome. These often relate to administrative requirements such as complex registration and licensing procedures, the need for the involvement of several institutions, or difficult procedures for access to relevant data. The paper concludes that discussed barriers will need to be carefully addressed if the high expectations for the role of energy communities are to be met.

Blockchain based decentralized local energy flexibility market

Large-scale deployment of renewable energy sources brings new challenges for smart grid management requiring the development of decentralized solutions and active participation of prosumer and non-grid-owned assets. Local energy flexibility markets can help in monitoring energy flows, motivate changes in prosumers’ energy supply and demand, achieving local energy balance, and optimization of electricity flows. In this paper, we propose a blockchain-based decentralized energy flexibility market enabling small-scale prosumers to trade in a peer-to-peer fashion their flexibility in terms of load modulation concerning the baseline energy profiles. We have defined an energy flexibility token for digitizing the flexibility of prosumers allowing to be traded on the market as an asset and self-enforcing smart contracts for decentralized market operation including functions such as the placement of flexibility bids/offers, trading session management, or energy and financial settlement of energy flexibility transactions. For matching the flexibility bids and offers, a solution based on a greedy heuristic and a bipartite graph is proposed for minimizing the number of flexibility transactions and reducing the blockchain-associated costs, while Oracles are used to assure its secure integration with the blockchain. The blockchain-based flexibility market was validated with the help of the Terni city Distribution System Operator, showing promising results in enabling the self-consumption of renewable energy generated in a small scale urban micro-grid considering live energy monitoring data, and in assuring the local balancing of the demand side in a simulated environment considering many market participants and historical energy data.

Peer-to-Peer energy trading strategy for energy balance service provider (EBSP) considering market elasticity in community microgrid

The peer-to-peer (P2P) energy trading technology can effectively promote the mutual exchange and complementation of energy among diversified prosumers in the community microgrid. To further improve the energy resource allocation performance of the P2P energy trading, an energy balance service provider (EBSP) can be involved in the market. EBSP is an independent commercial entity configured with high-capacity energy storage systems (ESS) or auxiliary power sources, participating in the P2P market for profit. The energy shifting ability of EBSP can further improve the overall benefit of the microgrid. However, the scale effect endows the EBSP with comparatively strong market power, which may impose influence on the equilibrium of the P2P market. From the perspective of EBSP, the P2P market can be regarded as a market with elasticity. In the existing researches on optimized pricing and trading strategies of the P2P market, such a characteristic was not fully considered, which may have negative impacts on the economic profit of the EBSP and the overall benefit of the microgrid. Therefore, a P2P energy trading strategy for EBSP considering market elasticity is proposed in this paper. A market equilibrium model for the community microgrid with EBSP is first established, which reflects the elasticity of the market. Then, an optimized pricing model and trading strategy are established and solved to maximize the EBSP’s profit. Numerical results show that the proposed method can improve the profit of EBSP, meanwhile reducing the energy dispatching cost of prosumers, achieving peak load shifting and renewable energy self-consumption of the community microgrid.

Enhancing PV Self-Consumption through Energy Communities in Heating-Dominated Climates

The European Union, in accordance with its decarbonization objectives, has enacted the Directive (EU) 2018/2001 and subsequently the Directive (EU) 2019/944 that legally recognizes and regulates the formation of citizen energy communities. These are believed to be key enablers for reducing buildings’ carbon footprint by allowing for a wider diffusion of on-site renewable energy generation and by maximizing renewable energy self-consumption. In this study, the benefits of the energy community are assessed through simulations of average Italian buildings of various sizes, different energy efficiency levels, equipped with a photovoltaic system and a heat pump-driven heating system, and located in heating-dominated climates. The work focuses on energy communities both at the apartment scale—i.e., in a multi-family building—and at the building scale—i.e., in a neighborhood. The net energy consumption, the self-consumption, and the self-sufficiency of all the possible energy communities obtainable by combining the different buildings are compared to the baseline case that is represented by the absence of energy sharing between independent building units. The energy community alone at both the building-scale and the neighborhood-scale increases self-consumption by up to 5% and reduces net energy consumption by up to 10%. However, when the energy community is combined with other maximization strategies such as demand-side management and rule-based control, self-consumption can be raised by 15%. These results quantify the lower bound of the achievable self-consumption in energy communities, which, in the rush towards climate neutrality, and in light of these results, could be considered among the solutions for rationalizing the energy consumption of buildings.

Peer-to-peer energy trading of net-zero energy communities with renewable energy systems integrating hydrogen vehicle storage

This study presents peer energy trading management approaches in a net-zero energy community with fundamental units of university campus, commercial office and high-rise residential building groups as per actual energy consumption and simulation data. Hybrid solar photovoltaic and wind turbine systems are developed for power supply to the diversified community integrated with three hydrogen vehicle storage groups based on the TRNSYS platform. An individual peer energy trading price model is proposed for the diversified community to allocate an individual peer trading price to each building group according to its intrinsic energy characteristic and grid import price. The time-of-use peer trading management strategies are further developed for both uniform and individual energy trading price modes to improve the grid flexibility and economy. The study results indicate that the peer energy trading management in the individual trading price mode improves the renewable energy self-consumption ratio by 18.76% and load cover ratio by 11.23% for the net-zero energy community compared with the peer-to-grid trading. The time-of-use trading management in the individual trading price mode can reduce the net grid import energy by 8.93%, grid penalty cost by 142.87%, annual electricity cost by 14.54%, and equivalent carbon emissions by 8.93% (982.36 tCO2), respectively. This comprehensive feasibility study on the typical community with the proposed peer trading price model and management strategies provides significant guidance for renewable energy and hydrogen storage applications in large-scale communities within high-density urban contexts.

Pluralising the European energy landscape: Collective renewable energy prosumers and the EU's clean energy vision

To fulfil the European Union's (EU) goal of providing ‘Clean Energy for All Europeans', a transformative shift from centralised, fossil-fuel based systems to decentralised systems based on renewable energy sources (RES) is envisaged. Keen to lead the clean energy transition while embedding technological innovation and elements of justice and equitability into the envisioned ‘Energy Union’, EU Member States need their citizens on board as active participants. Prosumerism or self-consumption is an important part of this citizen involvement. While the new EU regulatory framework for energy now recognises civic-inspired prosumer initiatives such as energy communities, little is known about the full range and diversity of collective actors in renewable energy self-consumption as well as how they engage with the changing energy system. This paper presents an exploratory categorisation of the different collective social actors that produce and consume energy from renewable sources, referred to as ‘collective RES prosumers’, aiming to clarify their participation in the energy landscape. We find six categories with different engagement and needs, which we relate to the EU's current framing of collective energy actors. We recommend fine-tuning policies to the different actors to support a true-to-vision transposition of the recently completed Clean Energy Package (CEP).