Local Energy Storage Research Articles

Assessing the impact of local energy generation and storage to achieve the decarbonization of the single-family housing stock in Germany

Assessing the impact of local energy generation and storage to achieve the decarbonization of the single-family housing stock in Germany

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

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.

Decentralized micro-energy storage capacity sharing within the residential community: an enhanced uniform price-based bidding framework

“Sharing economy” refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach.

Energy Harvesting and Storage with a High Voltage Organic Inorganic Photo‐Battery for Internet of Things Applications

Integrated local energy harvesting and storage is a critical prerequisite for energy autonomy of distributed sensing arrays required for the implementation of the internet of things (IoT). In this context, the monolithic integration of solar cells with metallic lithium‐based batteries into stacked high voltage photo‐batteries allows to provide said energy autonomy, with the smallest possible footprint and the highest resource efficiency. However current photo‐battery designs require additional electronics to control and match power output like maximum power point (MPP) tracking and overcharge protection. Herein a novel and compact monolithic photo‐battery design is provided, advantageously combining an organic solar cell with a NMC 622 versus metallic lithium‐based battery, matched in terms of VOC and cut‐off voltage, thereby achieving photo‐charging without any control electronics and energy release on demand at a very compact footprint of only 2.5 cm × 2.5 cm × 0.2 cm. Additionally, by applying a power profile simulating a temperature and humidity sensor over the course of a 24 h light and dark cycle, real world applicability of the photo‐battery in this type of applications is demonstrated.

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.

Enhancing Renewable Energy Use in Residential Communities: Analyzing Storage, Trading, and Combinations

Renewable energy resources, especially rooftop solar PV, have gained momentum during the past few years. However, the local consumption of PV power is limited due to the negative correlation between peak PV power and residential loads. Therefore, this study analyzes various cases to maximize the consumption of renewables in communities encompassing dwellings both with and without PV installations. The three cases considered in this study are local energy storage, community energy storage, and internal trading. A total of six cases are analyzed by evaluating these cases individually and in combinations. To achieve this, first, a generalized optimization model with specific constraints for each case is developed. Subsequently, different indices are devised to quantitatively measure trading with the grid and the consumption of renewables under varying cases. The performance of these different cases is analyzed for a community comprising five dwellings over a summer week. Furthermore, the performance of each case is evaluated for various seasons throughout the year. Additionally, a sensitivity analysis of different storage capacities (both local and community) is conducted. Simulation results indicate that community storage results in the highest renewable consumption if only one case is considered. However, the overall combination of internal trading and community storage results in the highest cost reduction, lowest dependence on the grid, and the highest consumption of renewables. Finally, a techno-economic analysis is performed on four widely used battery technologies, taking into account diverse cost and technical considerations.

Renewable energy planning in Africa: robustness of mean and extreme multi-model climate change patterns in solar PV and wind energy potentials

Increasing renewable sources in the energy mix is essential to mitigate climate change, not least in countries where the energy demand is likely to rise over the coming decades to reduce or even skip durations of time where fossils dominate. For Africa, solar photovoltaic (PV) and inland wind energy, combined with hydropower, provide significant and untapped potentials, whereas trends and robustness measures need further investigation. This study aims to gain insight into distributed trends in solar PV and wind energy potentials over Africa. This study employs relevant metrics, including relative change, model agreement, robustness, bias, and absolute levels for every available model combination and two climate scenarios, with energy planning purposes in mind. The study finds that regional climate models were the primary control of spatio-temporal patterns over their driving global climate model. Solar PV potentials show more coherence between models, a lower bias and general high potentials in most African regions than wind potentials. Favourable locations for inland wind energy include mainly the regions of greater Sahara and the Horn region. For wind and solar potentials combined, scattered locations within Sahara stand out as the most favourable across scenarios and periods. The analysis of minimum energy potentials shows stable conditions despite low potentials in certain regions. The results demonstrate a potential for solar and wind power in most of the African regions and highlight why solar and wind power or synergies of energy mix should be considered for local energy planning and storage solutions.

Optimal planning and configuration of adiabatic-compressed air energy storage for urban buildings application: Techno-economic and environmental assessment

As urbanization and demand for energy increase, the importance of localized renewable energy resources and energy storage system solutions becomes more prominent. Adiabatic-compressed air energy storage (A-CAES) has been identified as a promising option, but its effectiveness in decentralized applications is not widely concerned. This study aims to plan and design a decentralized A-CAES system to enhance its significance within urban building infrastructure. The focus is on the optimal design of a decentralized A-CAES system for urban building utilization through a comprehensive analysis of its thermodynamic, techno-economic, and environmental aspects. A sizing-designing approach inducing simulation and optimization is proposed to customize the A-CAES system according to the specific application requirement. To do so, this study investigates several energy management operation strategies (EMOS) toward different potential applications of decentralized A-CAES to maximize the system's value and adaptability during the project's lifetime. Multiple scenarios concerning managing solar PV-surplus power are proposed to investigate the overall performance of the proposed model. Additionally, a sensitivity analysis (post-optimization) is conducted to ensure the model's applicability across different case studies. The results demonstrate that an energy cost saving in the range of 0.015–0.021 $/kWh is achieved for the optimal hybrid system in which the A-CAES system is planned for solar photovoltaic (PV) integration and seasonal load shifting, leading to shaving the grid peak demand. Adopting such an A-CAES-PV hybrid system allows for achieving a 52 % electrical load management ratio and 65 % carbon emission reduction compared to the primary power system (grid). Furthermore, under the worst-case scenario (zero selling back), such an optimal hybrid energy system (HES) achieves a PV self-consumption rate of around 92 % and a payback time of 15.5 years. This analysis provides useful insights for policymakers, building owners, and energy planners interested in implementing sustainable and energy-efficient solutions, especially in the feasibility study phase.

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.

Resilience strategies for energy adequacy, between energy storage and conscious behaviours

This paper examines the technical and economic sustainability aspects of energy transition on two different scales. On the one hand, it investigates the topic of RES in the national energy system, describing structure, limits and possibilities. On the other hand, it presents the energy requalification case study of a small public building as an exemplary case of a systemic approach that adopts extraordinary solutions in terms of design process. The aim of the paper is to propose a methodological approach to improve site potential, assuming that short-term strategies must necessarily involve all the actors (including end-users) through local energy storage systems.

Enhancing energy system resilience using tidal stream energy

During autumn 2021, high global gas demand led to a 400% increase in imported wholesale gas prices in the UK. This coincided with (i) an extended period of low wind resource, where wind turbines provided 60% less energy than typical levels for the time of year, (ii) low nuclear power availability and (iii) depleting domestic natural gas reserves. These simultaneous events led to UK wholesale electricity prices more than doubling. The 2022 military and political impact of Russia’s invasion of Ukraine has led to additional increases in imported fuel prices. These types of disruption have the potential to cause damage worth years of sector revenues [1]. In the future, energy resilience challenges must be overcome whilst also achieving net-zero to limit global warming to within 1.5 degrees Celsius above pre-industrial levels, whilst electricity demand at least doubles.
 This research investigates the role of tidal stream energy in enhancing energy system resilience during periods when the energy system is highly stressed, such as during autumn 2021 in the UK. The concept of resilience refers to the ability of the system to survive strong and unexpected disruptions and to recover quickly afterward [1]. This research is motivated by the acknowledgement that the commercial viability of marine power relies on the sector being able to explain how it can provide contributions beyond decarbonisation [2].
 We build on an energy system modelling case study of the Isle of Wight [3], using the EnerSyM-RC energy system model. Wind data between 2012 – 2020 is analysed to identify and characterise periods of low wind resource. These data form the inputs to the EnerySyM-RC modelling. The model then applies a simple brute force optimisation method to investigate the most suitable mix of solar PV, offshore wind, tidal stream and energy storage that enhances system resilience on the Isle of Wight.
 Initial results show that the adoption of tidal stream energy compliments solar and wind generation by reducing reliance on reserve energy (e.g. imported gas) during high-stress periods. Reduced reliance on imported energy is also achieved because of the distinct generation pattern of tidal power (i.e. 4 periods of power generation, each separated by slack tide, every day), which enhances the utilisation of local energy storage.
 References
 [1] Jasiunas J, et al., 2021, Energy system resilience – A review, Renewable and Sustainable Energy Reviews, 150:111476
 [2] Pacific Northwest National Laboratory, 2021, Grid Value Proposition of Marine Energy: A Preliminary Analysis, PNNL -31123, Technical report
 [3] Coles DS et al., 2023, Impacts of tidal stream power on energy system security: An Isle of Wight case study, Applied Energy, 334:120686

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.

COST-WINNERS: COST reduction WIth Neural NEtworks-based augmented Random Search for simultaneous thermal and electrical energy storage control

The combination of local renewable energy production, dynamic loads, and multiple energy storage systems with different dynamics requires sophisticated control systems to maximize the energy cost efficiency of the combined energy system. Battery and thermal energy storage systems can be combined to increase the local use of on-site renewable energy, reduce peak power demand, and exploit time-of-use energy pricing. In this paper, we focus on how the augmented random search algorithm and artificial neural networks can be used together to solve an energy cost optimization problem involving the control of a battery energy storage system and a thermal energy storage system at the same time in a smart warehouse. As part of this work, a simulated training environment made using the data from the smart warehouse’s operations. In addition to the energy storage systems, the warehouse energy system has integrated a large roof mounted photovoltaic power plant and an industrial-scale cooling system.The developed solution is able to minimize the energy costs by modulating both energy systems, depending on the situation. Additionally, when it is tested against the state-of-the-art solutions, our developed solution at worst matches performance when the alternative algorithm is allowed to increase training time by a factor of nearly three. On average, our presented solution doubles the performance of the benchmark algorithm with much less computational resource expenditure.

Impacts of economic regulation on photovoltaic distributed generation with battery energy storage systems

Photovoltaic systems are largely involved in the process of decarbonization of the electricity production. Among the solutions of interest for deploying higher amounts of photovoltaic (PV) energy generation for reducing the electricity taken from the grid, the inclusion of local battery energy storage systems has been considered. Battery energy storage provides an energy buffer useful to better manage the fluctuations of PV energy production, or to serve the demand when the PV generation is absent or insufficient and the price of the electricity taken from the grid is high. While technically sound, the installation of a PV system with battery energy storage has to demonstrate its profitability in the specific context of application, also depending on the regulation in place in the relevant jurisdiction. This paper presents the stochastic economic feasibility analysis for the installation of distributed photovoltaic power plants facing the new Brazilian regulation of electric energy compensation system, and also considers the hourly tariff known as White Tariff. Three different sizes of distributed power plants are proposed, and the related models introduce battery banks to regulate the peak demand when tariffs are more expensive. In the absence of economic incentive policies to support this kind of renewable energy generation associated with battery energy storage systems, there is a lower probability of economic viability, especially for micro-plants up to 10 kW of installed power.

Effect of micron-scale nonmetallic inclusions on fatigue crack nucleation in a nickel-based superalloy

Micron-level nonmetallic inclusions are likely to be a limiting factor for further improvement in the fatigue resistance of nickel-based superalloys. To investigate the fatigue response of nickel-based superalloys containing micro-inclusions, a dislocation-based crystal plasticity model was employed for numerical simulations, and the local energy storage density criterion was used for damage evolution analysis. The results showed that, as a hard phase, micro-inclusions interfered with the plastic deformation of the matrix, as evidenced by the inhomogeneous slipping of the main slip systems and the activation of new slip systems. Additionally, micro-inclusions induced the accumulation of geometrically necessary dislocations (GNDs) around them. When micro-inclusions appeared on grain boundaries, the above factors changed the energy required for local deformation through potential hardening effects in the model and ultimately affected the evolution of the local energy storage density. When micro-inclusions appeared inside grains, the evolution of local energy storage was often dominated by plastic deformation only. The above mechanism is microstructure sensitive and spatially limited, which leads to the dispersion of lifetimes and negligible effect of micro-inclusions on distal grains.

DC Link Voltage Enhancement in DC Microgrid Using PV Based High Gain Converter with Cascaded Fuzzy Logic Controller

Renewable-based sources can be interconnected through power electronic converters and connected with local loads and energy storage devices to form a microgrid. Nowadays, DC microgrids are gaining more popularity due to their higher efficiency and reliability as compared to AC microgrid systems. The DC Microgrid has power electronics converters between the DC loads and renewable-based energy sources. The power converters controlled with an efficient control algorithm for maintaining stable DC bus voltage in DC microgrids under various operating modes is a challenging task for researchers. With an aim to address the above-mentioned issues, this study focuses on the DC link voltage enhancement of a DC Microgrid system consisting of PV, DFIG-based wind energy conversion system (WECS), and battery Energy Storage System (ESS). To elevate PV output voltage and minimize the oscillations in DC link voltage, a high-gain Luo converter with Cascaded Fuzzy Logic Controller (CFLC) is proposed. Droop control with virtual inertia and damping control is proposed for DFIG-based WECS to provide inertia support. Artificial Neural Network (ANN) based droop control is utilised to regulate the ESS’s State of Charge (SOC). The effectiveness of the proposed converter and its control algorithms for maintaining stable DC bus link voltage has been analysed using MATLAB/Simulink and experimentally validated using a prototype model and FPGA Spartan 6E controllers.

Analysing Grid-Level Effects of Photovoltaic Self-Consumption Using a Stochastic Bottom-up Model of Prosumer Systems

Self-consumption of the energy generated by photovoltaics (PV) is playing an increasingly important role in the power grid. “Prosumer” systems consume part of the produced energy directly to meet the local demand, which reduces the feed-in into as well as the demand from the grid. In order to analyse the effects of PV self-consumption in the power grid, we introduce a stochastic bottom-up model of PV power generation and local consumption in the control area of the German transmission system operator TransnetBW. We set up a realistic portfolio of more than 100,000 PV/prosumer systems to generate representative time series of PV generation and consumption as a basis to derive self-consumption and feed-in. This model allows for the investigation of the time-dependent behaviour in detail for the full portfolio whereas measurements are presently only available as aggregated feed-in time series over a nonrepresentative subset of systems. We analyse the variation of self-consumption with PV generation and consumption at the portfolio level and its seasonal, weekly and diurnal cycles. Furthermore, we study a scenario of 100% prosumers as a limiting case for a situation without subsidized feed-in tariffs and local energy storage.

Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station

Electric vehicles (EVs) are considered as the leading-edge form of mobility. However, the integration of electric vehicles with charging stations is a contentious issue. Managing the available grid power and bus voltage regulation is addressed through renewable energy. This work proposes a grid-connected photovoltaic (PV)-powered EV charging station with converter control technique. The controller unit is interfaced with the renewable energy source, bidirectional converter, and local energy storage unit (ESU). The bidirectional converter provides a regulated output with a fuzzy logic controller (FLC) during charging and discharging. The fuzzy control is implemented to maintain a decentralized power distribution between the microgrid DC-link and ESU. The PV coupled to the DC microgrid of the charging station is variable in nature. Hence, the microgrid-based charging is examined under a range of realistic scenarios, including low, total PV power output and different state of charge (SOC) levels of ESU. In order to accomplish the effective charging of EV, a decentralized energy management system is created to control the energy flow among the PV system, the battery, and the grid. The proposed controller’s effectiveness is validated using a simulation have been analyzed using MATLAB under various microgrid situations. Additionally, the experimental results are validated under various modes of operation.

Multilevel optimization of economic dispatching in active distribution network based on ADMM

With the continuous improvement of the penetration rate of renewable energy and the continuous integration of advanced network control technology and measurement equipment, the traditional distribution network is developing into an active distribution network (ADN) with the characteristics of flexible scheduling control, high user interaction, and high energy utilization. This article fully considers the economy of the overall operation of the distribution network, and proposes a hierarchical optimization economic dispatch method for active distribution networks based on the alternating direction multiplier method (ADMM). Firstly, a hierarchical optimization scheduling model of active distribution network is established with the goal of minimizing the overall operating cost of the distribution network. The alternating direction multiplier method algorithm is decomposed into upper and lower layers to solve. The upper layer is optimized with the goal of minimizing the overall operating cost of the distribution network, and the lower layer considers the distribution network. The distributed photovoltaic and energy storage units connected to the internal nodes of the network are optimized with the goal of minimizing the local energy storage operation cost and power purchase cost. The upper and lower layers, through the exchange of limited boundary information, iterate each other until the convergence conditions are met, and the optimal solution is obtained. Finally, a design example is tested to verify the effectiveness and feasibility of the proposed scheduling method.