Local Energy Generation Research Articles

Federated learning assisted distributed energy optimization

AbstractThe increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by multiple entities (agents). The transactive energy community serves as an established example of this transition. Distributed energy management approaches can effectively address the evolving grid's scalability, resilience, and privacy requirements. In this context, the accuracy of agents' estimations becomes crucial for the performance of distributed and multi‐agent decision‐making paradigms. This paper specifically focuses on integrating federated learning (FL) with the multi‐agent energy management procedure. FL is utilized to forecast agents' local energy generation and demand, aiming to accelerate the convergence of the distributed decision‐making process. To enhance energy aggregation in transactive energy communities, we propose an FL‐assisted distributed consensus + innovations approach. The results demonstrate that employing FL significantly reduces errors in predicting net power demand. The improved forecast accuracy, in turn, introduces less error in the distributed optimization process, thereby enhancing its convergence behaviour.

Stochastic Optimal Strategies and Management of Electric Vehicles and Microgrids

This study combines the Nash–Cournot competition model and the stochastic optimization model to examine the impact of electric vehicle (EV) quantity fluctuations on microgrid operations, aiming to optimize energy usage in a competitive electricity market. Integrating distributed energy resources and bidirectional charging, microgrids offer a novel approach for energy optimization, aiding in renewable energy generation, peak demand management, and emission reduction. Empirical evidence highlights benefits in Taiwan’s electricity market and net-zero emissions target by 2050, with a case study demonstrating enhanced local renewable energy generation due to EVs and microgrid integration. As the number of EVs increases, electricity sales from microgrids decline, but electricity purchases remain stable. The degree of electricity liberalization also influences the supply and demand dynamics of the electricity market. Microgrids selling electricity only to the main grid increases total power consumption by 65.55 million MWh, reducing the market share of the state-owned utility (Taipower). Conversely, allowing retailers to purchase from microgrids increases total consumption by 30.87 million MWh with a slight market share decrease for Taipower. This study contributes to providing an adaptable and flexible general model for future studies to modify and expand based on different scenarios and variables to shape energy and environmental policies.

Research on the Economic Environmental Impact and Carbon Reduction Pathways of Power Battery Industry Development - A Case Study of Yibin City

Abstract The power battery industry in China is experiencing rapid development, with the swift expansion of capacity potentially posing challenges to environmental protection and carbon reduction. Using the development plan of the power battery industry in Yibin City as a case study, this paper employs the Material Flow Analysis and Tapio decoupling model to deeply explore the economic benefits and environmental burdens brought by the development of power battery key industry under scenarios, proposing optimized pathways to promote low-carbon industrial development. The results indicate that the industrial output value can increase from 92.1 billion CNY in the baseline year to 382.6 billion CNY in 2030, with carbon emissions rising from 0.55 million tons to 2.32 million tons. The adjustment of product structure has limited effect on carbon reduction in the power battery industry. In the short term, optimizing the energy structure has greater carbon reduction potential than improving energy efficiency, and clean energy utilization has the highest marginal carbon reduction effect. Implementing measures to optimize the energy structure and improve efficiency simultaneously can achieve weak decoupling of economic growth and carbon emissions, though strong decoupling is challenging to attain. The proportion of local renewable energy generation also impacts the low-carbon capability of the industry, suggesting that regions with abundant renewable energy should be prioritized for industrial layout. Promoting the decoupling of economic growth and environmental impact in the power battery industry can be facilitated by supporting environmental governance infrastructure, driving the green energy transition, and encouraging low-carbon technological innovation. JEL classification numbers: F062.2 Keywords: Power battery, Scenario analysis, Material flow, Economic environmental impact, Carbon emissions.

Towards a positive energy balance: A comparative analysis of the planning and design of four positive energy districts and neighbourhoods in Norway and Sweden

Positive energy districts and sustainable plus energy neighbourhoods are developed in the European context to reduce energy use and greenhouse gas emissions from the building sector. The planning and development of positive energy districts and sustainable plus energy neighbourhoods is complex and requires collaboration between stakeholders and new measures to achieve high energy efficiency, local renewable energy generation, energy storage and flexibility, and energy sufficiency. This paper examines the implementation of energy measures in the planning and design of four positive energy district and neighbourhood development projects in Norway and Sweden. The paper compares the two different institutional and energy system contexts and how these affect the development of positive energy districts, focusing on the perspectives of the municipality and developers. Existing academic literature and positive energy district guidelines are used to develop an analytical framework for the planning and design of positive energy districts and sustainable plus energy neighbourhoods. Results highlight an early focus on energy ambitions, wide stakeholder involvement, and the importance of aligning interests between stakeholders and working interdisciplinary in the planning and design phases to find optimal energy measures. Both the building and the neighbourhood/district level are important to increase energy efficiency, energy sufficiency, and energy flexibility, and consequently lower the environmental impact of the whole development project.

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

In the dynamic landscape of renewable energies, microgrid systems emerge as a promising avenue for fostering sustainable local energy generation. However, the effective management of energy resources holds the key to unlocking their full potential. This study assumes the task of creating a multi-objective optimization algorithm for microgrid energy management. At its core, the algorithm places a premium on seamlessly integrating renewable energy sources and orchestrating efficient storage coordination. Leveraging the prowess of a multi-agent system, it allocates and utilizes energy resources. Through the combination of renewable sources, storage mechanisms, and variable loads, the algorithm promotes energy efficiency and ensures a steady power supply. This transformative solution is underscored by the algorithm's remarkable performance in practical simulations and validations across diverse microgrid scenarios, offering a prevue into the future of sustainable energy utilization.

Unraveling the implementation processes of PEDs: Lesson learned from multiple urban contexts

The implementation of Positive Energy Districts (PEDs) is recognized as a promising approach to achieving energy efficiency and reducing the negative environmental impact of climate change through the surplus of local renewable energy generation. However, several barriers to the implementation of PEDs, coupled with the lack of a joint definition and clarity surrounding PEDs, need to be addressed. These barriers include governance, incentives, social, process, market, technology, and context challenges, requiring a profound understanding of the priorities, ambitions, strategies, contextual conditions, administrative conditions, policies, economic and technical resources, and existing solutions of cities.This study explores the creation and implementation of PEDs, seeking to uncover the potential and challenges of this innovative concept in the pursuit of climate neutrality and energy efficiency. Through a peer-to-peer analysis of PED case studies and qualitative interviews with key stakeholders in Brussels, Stockholm, Vienna, Évora, Lisbon, and Salzburg, challenges such as the lack of clarity in the definition of PEDs, diversity of ownership, administrative complexity, resistance to change, limited knowledge exchange, financing constraints, technological limitations, and inadequate involvement of relevant actors are identified. Moreover, success factors and enabling strategies from these case studies are highlighted, including clear roadmaps, stakeholder collaboration, integrated decision-making processes, political commitment, and coordination platforms.

Decomposed particle swarm optimization for optimal scheduling in energy hub considering battery lifetime

The energy hub has become pivotal in optimizing multi-carrier energy systems, aiming to enhance the flexibility and efficiency of the overall infrastructure. By incorporating energy storage within these hubs, it becomes feasible to dynamically coordinate the balance between energy generation and consumption. This coordination considers diverse energy pricing structures and local renewable energy generation, leading to substantial reductions in energy expenditures. To further amplify the long-term utilization of batteries, it is essential to account for the associated costs related to their lifespan during the optimization process. Nonetheless, optimizing the operation of an interconnected energy hub system while factoring in battery lifespan introduces a complex challenge, encompassing various limitations, spanning multiple time frames, and involving non-convex aspects. This article introduces an inventive strategy termed the “decomposition technique,” which entails amalgamating particle swarm optimization with a numerical approach. By disassembling the complex optimization predicament into more manageable sub-problems, particularly the organization of storage and other elements within the energy hub system, this approach capitalizes on the individual strengths of particle swarm optimization and the interior point method. To assess the efficacy of this proposed method, it is applied across various scenarios, encompassing a two-hub system and a three-hub system spanning a 24-h timeframe. Through comparison with analytical methods, the results obtained affirm the technique's capability to converge remarkably close to the global minimum. Moreover, the computational efficiency of this approach enables its online implementation, with a reduced time horizon, and ensures real-time optimization. The success of this approach opens up potential applications in diverse energy hub systems and emphasizes its adaptability and scalability. Using the strengths of particle swarm optimization and the interior point method, this technique provides an effective solution for optimizing energy hub systems while considering battery lifetime costs. It enables near-optimal performance while overcoming limitations related to convergence and computation time. The findings of this research contribute to the broader field of energy optimization and pave the way for more efficient and sustainable energy management strategies. The decomposition technique yielded a significant 12% reduction in total energy cost compared to conventional particle swarm optimization. In the three-hub system, the decomposition technique showcased a 15% increase in computational efficiency, affirming its adaptability for larger systems. Considering battery lifetime costs, excluding them led to a 6% boost in energy savings but resulted in a 2% rise in overall system cost, emphasizing the need for a balanced optimization approach.

Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization

This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical formulations of the operating resistance in SAF are explored, and deep neuron networks are employed and modified for dynamic uncertainty prediction. The proposed approach is validated through a case study involving an intelligent energy terminal with a 12.5 MVA SAF system and 12 MW capacity renewable generators in an electricity market with fluctuating prices. The findings of this research underscore the efficacy of the proposed optimization model in reducing operational costs and enhancing the utilization of localized renewable energy generation. By integrating four distinct dissatisfaction coefficients into the optimization framework, we demonstrate the model's adaptability and efficiency. The application of the optimization strategy delineated herein results in the SAF system's profitability oscillating between $111 and $416 across various time intervals, contingent upon the coefficient settings. Remarkably, an aggregate daily loss recovery amounting to $1,906.84 can be realized during the optimization period. Such outcomes not only signify considerable economic advantages but also contribute to grid stability and the diminution of renewable energy curtailment, thereby underscoring the dual benefits of economic efficiency and sustainability in energy management practices.

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

Energy transition plans: How municipalities face the energy transition

Energy transition plans: How municipalities face the energy transition Municipalities need tools to manage and achieve the local energy transition, according to Gerard Laguna Benet, Researcher at BEE Group – CIMNE. The European Union (EU) takes significant steps towards decarbonisation,aiming to be climate-neutral by 2050. To reach this ambitious target, the EU introduced a roadmap which set a 55% reduction of greenhouse gas emissions, above 32% share of renewable energy and at least 32.5% of energy efficiency improvement by 2030.In this context, the municipalities have a significant role as the energy transition pushes Europe to a decentralised and local energy generation framework.On one side, municipalities have a role to play in taking exemplary action to reduce energy consumption in their buildings. On the other side, to lead Europe towards climate neutrality, it’s essential to make efforts at the local level, considering each region’s specific characteristics and challenges. Therefore, municipalities have a vital role in the European energy transition.

Reducing the carbon footprint of Whisky production through the use of a battery and heat storage alongside renewable generation

This paper presents an analysis of providing a typical distillery with low carbon energy through the combination of local wind energy, solar PV, electricity storage and heat storage.The aim of this is to increase the sustainability of the energy-intensive whisky industry. Using hourly local renewable resource data and typical distillery consumption information, the local energy generation is balanced against the demand at the time of use. This followed by load shifting using a battery and heat storage. Results show that significant carbon savings can be achieved by a carefully designed portfolio of hybrid generation, battery storage and heat storage.

Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities

Peer-to-peer (P2P) energy trading and energy communities have garnered much attention over in recent years due to increasing investments in local energy generation and storage assets. Much research has been performed on the mechanisms and methodologies behind their implementation and realisation. However, the efficiency to be gained from P2P trading, and the structure of local energy markets raise many important challenges. To analyse the efficiency of P2P energy markets, in this work, we consider two different popular approaches to peer-to-peer trading: centralised (through a central market maker/clearing entity) vs. fully decentralised (P2P), and explore the comparative economic benefits of these models. We focus on the metric of Gains from Trade (GT), given optimal P2P trading schedule computed by a schedule optimiser. In both local market models, benefits from trading are realised mainly due to the diversity in consumption behaviour and renewable energy generation between prosumers in an energy community. Both market models will lead to the most promising P2P contracts (the ones with the highest Gains from Trade) to be established first. Yet, we find diversity decreases quickly as more peer-to-peer energy contracts are established and more prosumers join the market, leading to significantly diminishing returns. In this work, we aim to quantify this effect using real-world data from two large-scale smart energy trials in the UK, i.e. the Low Carbon London project and the Thames Valley Vision project. Our experimental study shows that, for both market models, only a small number of P2P contracts i.e. less than 10% of the possible P2P contracts are required to achieve the majority of the maximal potential Gains from Trade. Similarly, only a fraction of prosumers are required to participate in energy trading to realise significant GT; namely we found that 60% of the maximal GT can be realised with only 30% of prosumers’ participation, with the percentage of maximal GT reaching 80% when participation increases to 50% of prosumers. Finally, we study the effect that diversity in consumption profiles has on overall trading potential and dynamics in an energy community. We show that in a community with a DF(load diversity factor) = 1, 80% of potential maximal GT can be achieved by 10% of prosumers engaging in P2P trading, while in a community with DF = 1.5, it is beneficial for 40% of the prosumers to trade.

Design and Development of Alternative Energy Sources: Solar Power Plant in Kalandrina Selosia Residential Area RT 008 / RW 008 Jayamukti Village

Purpose: This research paper aims to introduce a solar power plant in Kalandrina Selosia's residential area, addressing local energy needs sustainably. The study's significance lies in offering a renewable energy solution within a community context.
 Method: The research used experimental and descriptive methods to measure solar panel output and intensity alongside assessing electrical installations. Analytical techniques evaluated the system's functionality.
 Practical Applications: The designed 50-watt solar power plant showcases the feasibility of local solar energy generation. Its potential to reduce costs and environmental impact holds practical implications for communities lacking conventional power sources.
 Conclusion: This study successfully demonstrates solar power's viability in Kalandrina Selosia. The project contributes practical insight into renewable energy adoption, benefiting the community and broader understanding.

A semi-distributed charging strategy for electric vehicle clusters

Uncoordinated charging of electric vehicle (EV) clusters can lead to unanticipated surges in demand that strain the grid, hence reducing its reliability. To address this challenge, this paper proposes a semi-distributed control method for EV clusters charging where the decision-making process takes place locally, rather than relying on an external controller. The method ensures that charging demand can be fulfilled while achieving several goals, including following local renewable energy generation, pausing charging for transformer protection, and continuing charging progress in the absence of external information. The proposed method is validated through simulations and experimental tests during a public demonstration. The results demonstrate the functionality of the control method and show that it can effectively manage the charging of a cluster of EVs while reducing strain on the power grid.

In-state generation requirements and the acceptability of renewable portfolio standards

Renewable Portfolio Standard (RPS) programs are a primary renewable energy policy. We design an experimental survey to examine how public support for RPS programs is affected by requiring in-state renewable energy generation to meet renewable targets. We consider how the influence of this design feature matters across key voter characteristics. Overall, we find strong support for RPS programs and that majority support often depends on the RPS program requiring local renewable energy generation. We find that environmental concerns drive support while economic concerns drive opposition, with local generation requirements boosting support among those concerned about local issues.

SELECTION OF THE NON-ISOLATED UNIDIRECTIONAL DC-DC CONVERTERS FOR PHOTOVOLTAIC APPLICATIONS

The rising prominence of renewable energy sources and the demand for efficient power distribution has led to the emergence of DC microgrids as a promising solution for localized energy generation and distribution. The effective operation of DC microgrids heavily relies on the performance of their DC-DC converters, which play a crucial role in regulating power flow and voltage. Selecting suitable DC-DC converters is thus essential to ensure optimal performance and stability of photovoltaic systems. This academic article presents a comprehensive comparative analysis of different non-isolated DC-DC converter topologies and their appropriateness for DC microgrid applications. The research delves into various converter types, including buck, boost, buck-boost, SEPIC, and Zeta converters, thoroughly examining their operating principles, control strategies, and advantages within the context of DC microgrids. The focus is on key performance metrics essential for a comprehensive evaluation, including efficiency, voltage regulation, transient response, power density, and cost-effectiveness. Through meticulous comparative analysis, it is established that the SEPIC and Zeta converters stand out as exceptional choices for DC microgrids. Their distinct advantages, such as reduced output ripple, improved efficiency, and wide input voltage range, position them as valuable contenders for modern power electronics applications, showcasing their excellence in addressing diverse voltage regulation needs. This academic article contributes valuable insights into the selection and application of DC-DC converters in DC microgrid systems, providing researchers, engineers, and industry stakeholders with a comprehensive understanding of the comparative advantages of these converters. The findings underscore the significance of choosing appropriate converters to achieve efficient energy conversion, optimal power distribution, and the advancement of renewable energy technologies in the context of DC microgrids. Keywords: DC-DC converters, SEPIC, ZETA, BUCK, BOOST.

Enhancing of the power system resilience through the application of micro power systems (microgrid) with renewable distributed generation

The power sector plays a critical role in the functioning of the economy and the security of a country, being closely interconnected with other vital infrastructures, such as gas supply, water supply, transportation, and telecommunications. Ensuring a stable power supply is crucial for the uninterrupted operation of these systems. One way to enhance the resilience of the power system is by integrating local networks with distributed renewable generation into the overall energy infrastructure. The flexibility, stability, controllability, and self-healing capabilities of microgrids make them an effective solution for improving the resilience of the power system. The power grid is susceptible to disturbances and disruptions that can cause large-scale power outages for consumers. Statistical data indicates that approximately 90% of outages occur due to issues in the distribution system, thus research focuses on local microgrids with distributed renewable generation. This study analyzed the role of microgrids with renewable generation in enhancing the resilience of power systems. Additionally, functions of microgrids that contribute to enhancing power system resilience, such as service restoration, network formation strategies, control and stability, as well as preventive measures, were summarized. It was found that local microgrids have significant potential to enhance power system resilience through the implementation of various strategies, from emergency response planning to providing reliable energy supply for quick responses to military, environmental, and human-induced crises. The concept of local distributed energy generation, storage, and control can reduce reliance on long-distance power transmission lines, reduce network vulnerabilities, and simultaneously improve its resilience and reduce recovery time. It has been determined that the most necessary and promising approaches to enhance the resilience of the power system include developing appropriate regulatory frameworks, implementing automatic frequency and power control systems, ensuring resource adequacy (including the reservation of technical components), promoting distributed generation, integrating energy storage systems into the energy grid, and strengthening cyber security. Keywords: resilience, local power systems, MicroGrid, distributed generation, renewable energy sources.

Representing decentralized generation and local energy use flexibility in an energy system optimization model

Local energy generation and energy use flexibility is becoming increasingly relevant for planning energy systems with a high level of renewables, electrification, and decentralization. Although current energy system models represent various flexibility options, capturing demand-side flexibility and prosumption remains challenging. In light of these challenges, this study presents a national-scale energy system optimization model covering the residential and commercial sectors of Belgium and considering the interplay between local energy use flexibility from electric vehicle (EV) charging, battery storage, rooftop photovoltaic (PV) systems and distribution grids. To adequately represent energy use flexibility, the model adopts a high temporal resolution, consisting of three representative weeks at an hourly time scale. The role of prosumption is analysed by introducing a prosumer potential reflecting the extent to which local energy production can be used to meet local demand ‘behind the meter’. The results show that flexibility significantly reduces total system costs with reduced distribution grid capacity investment by 48% by 2050. Furthermore, local energy use flexibility enables a significant uptake of rooftop PV (+6.7 GW), but only under a high prosumer potential condition. Finally, a curtailment of around 5–7% of PV production is cost-effective to sustain a more continuous use of the distribution grid at low peak capacity. To increase the robustness of the results, future research should consider expanding the model's sector coverage, adding additional flexibility sources, improving the representation of prosumption, soft-linking with distribution grid models and a better representation of mobility patterns.

Model predictive control for smart grid charging of autonomous electric vehicle fleet using local renewable energy generation

Model predictive control for smart grid charging of autonomous electric vehicle fleet using local renewable energy generation

A Systematic Review of Optimization Approaches for the Integration of Electric Vehicles in Public Buildings

Electric vehicles (EVs) can provide important flexibility to the integration of local energy generation in buildings. Although most studies considering the integration of EVs and buildings are focused on residential buildings, the number of publications regarding large buildings, in particular, public buildings (PBs), has increased. However, the quantity of studies regarding the integration of EVs and PBs is still limited. Additionally, there are no review studies approaching the integration of EVs and buildings in one single framework. In this sense, this review aims to address the challenges and trends associated with optimizing the charging of EVs in PBs by conducting a systematic review of the existing literature. As contributions, this work develops a review that approaches the integration of EVs and PBs using multiple strategies and structures, presents an integrated picture of the technical and economic constraints, and addresses the future trends and research perspectives related to the subject. Through the use of an open-access search engine (LENS), a cluster of 743 publications was analyzed using two strings and a timeframe restriction. The most important contributions regarding optimization strategies and their evolution are presented, followed by a comparison of the findings with other review papers. As key findings, technical and economic constraints are identified (uncertainties of driving behavior and local generation, battery degradation, “injection tariffs”, etc.), as are future trends and perspectives (local generation legislation, incentives for purchasing EVs, energy communities, etc.).