Deploying Energy Storage Research Articles

Energy Storage Integration for Renewable Energy Supply in Malaysia: A Review

Malaysia intends to increase the share of renewable energy (RE) in its installed power capacity from the current 23%, or 8.5 GW, to 40%, or 18.0 GW, by 2035. Renewable energy sources, specifically solar and biogas, are unpredictable and subject to sudden changes due to weather conditions and feedstock availability. Furthermore, their integration with the current conventional plants is complicated. In relation to this, deploying energy storage has become the main agenda under the Renewable Energy Roadmap to achieve 40% installed capacity by 2035. When selecting the most adequate energy storage system, the technological, environmental and economic aspects need to be considered. Therefore, the purpose of this article is to review the current state of energy storage and the renewable energy situation in Malaysia. The significance of this paper is that it encourages RE consumption in Malaysia in parallel with the government’s target due to the deployment of energy storage in the energy supply system. It can serve as a guideline for policymakers in their penetration of the renewable energy sector.

Role of Indian power markets in energy transition

India's power sector is transforming rapidly, marked by rising demand and a growing share of renewables. The short-term power market provides option for power distribution utilities to meet demand exigency. The paper presents trends and perspectives of short-term electricity trading of select states of India. Market transactions reflect seasonal demand variations, higher during summer-monsoon months, dipping in winters. Analysis of data highlights substantial share of short-term power in the total procurement. Quantum of power traded correlates inversely with operational performance of thermal plants as well as grid share of renewable energy. Suggested policy measures include honing of demand forecasting skills using advanced metering infrastructure, deploying energy storage technologies, including decentralized solar, ensuring sustained supply of coal and preventive maintenance of thermal power plants, initiating seasonal procurement contracts with merchant power plants, encouraging power banking arrangements and including storage component in solar and wind tenders. Power markets, if employed appropriately, can help minimize the demand-supply gap, facilitate energy access, and accommodate higher quantum of green power, thus fulfilling SDG-7 targets.

Improving resilience of cyber–physical power systems against cyber attacks through strategic energy storage deployment

The widespread integration of advanced technologies has increased the vulnerability of Cyber–Physical Power Systems (CPPS) against cyber attacks. In addition, the incorporation of renewable energy sources (RESs) poses extra uncertainty challenges in CPPS security. In this paper, focusing on resilience assessment and improvement, we first propose a comprehensive framework to reveal the whole process from attack initiation to performance recovery. Then, regarding the protection strategies before and after cyber attacks, we improve resilience performance by protecting critical nodes and deploying energy storage nodes. The effectiveness of our proposed methods is validated through testing on a simulated model, which couples the IEEE 39-bus power system with a scale-free communication network. The results demonstrate that the deployment of energy storage plays a significant role in suppressing the uncertainty of RESs and improving the resilience of CPPS against cyber attacks. In addition, we employ a heuristic algorithm to optimize the placement of energy storage nodes. Our work not only represents an overview of the resilience process but also provides system dispatchers with practical strategies for improving the resilience of renewable CPPS against the growing threat of cyber attacks.

Energy storage as a transmission asset: Definitions and use cases

There is growing interest in deploying energy storage as a transmission asset (SATA), as evidenced by an evolving body of supportive policies and regulations and an expanding body of literature on the topic. Despite nearly two decades of evolution, however, transmission planning processes in the United States rarely consider storage alternatives and only a handful of projects have been selected. Regulators have assigned the responsibility of proposing non-transmission alternatives to non-utility participants in the planning process, so the sparse record of SATA projects may therefore result from a lack of specificity about the use cases for SATA and uncertainty about when it should be proposed. Extensive literature has been devoted to the topic of SATA, but much of it relies on definitions that are inconsistent with regulatory precedent and that exacerbate confusion. This paper reviews regulatory proceedings to define three types of energy storage assets than can interact with the transmission system: storage as a transmission asset, storage in place of a transmission asset (SIPTA), and dual-use energy storage. It then provides an inventory of use cases for SATA and SIPTA projects, using brief case studies to illustrate each use case. By clarifying the definition of SATA and its roles in transmission system planning, this work may be of use to transmission system planners and other participants in transmission planning processes to identify situations in which SATA alternatives merit consideration.

Optimization method for capacity of BESS considering charge‐discharge cycle and renewable energy penetration rate

AbstractIn order to achieve the “carbon peaking and carbon neutrality” goals, we must vigorously develop renewable energy power generation. As the penetration of renewables progressively escalates, the corresponding demand for battery energy storage systems (BESS) within the power grid rises concomitantly. This paper presents an innovative optimization approach for configuring BESS, taking into account the incremental variations in renewable energy penetration levels and BESS charge‐discharge cycles. Employing incremental analytical techniques and pivotal metrics such as capacity elasticity, the proposed method determines the optimal penetration rate and corresponding BESS capacity outcomes for deploying energy storage systems. An example analysis of a rural power distribution benchmark is carried out by using the method in this paper, which proves the effectiveness of the method in this paper. This methodology was substantiated through its application to a case study of a rural power distribution benchmark, thereby validating its efficacy. Furthermore, it was compared with the particle swarm optimization, providing a comparative assessment of their relative performance.

Requirement on the Capacity of Energy Storage to Meet the 2 °C Goal

The inherent power fluctuations of wind, photovoltaic (PV) and bioenergy with carbon capture and storage (BECCS) create a temporal mismatch between energy supply and demand. This mismatch could lead to a potential resurgence of fossil fuels, offsetting the effects of decarbonization and affecting the realization of the Paris target by limiting global warming to below 2 °C in the 21st century. While application of energy storage is widely recommended to address this limitation, there is a research gap to quantify the impacts of energy storage limitation on global warming. Here, we analyzed the hourly variation of global wind and PV power during the period 1981–2020 and the monthly capacity of biomass production in 2019, and thus quantified the impact of decreasing the capacity of energy storage on global warming using a state-of-the-art Earth system model. We found that global warming by 2100 in the SSP1-2.6 scenario would increase by about 20% and exceed 2 °C without deploying energy storage facilities. Achieving the 2 °C target requires reducing power losses of wind and PV by at least 30% through energy storage. This requirement delivers to a cumulative storage capacity of 16.46 TWh using batteries during the period 2021–2100, leading to the international trade of cobalt and manganese across countries due to deficits of minerals at a country level. In the context of energy security, we highlight the importance of considering the limitations of energy storage and mineral shortage in the forthcoming policies of decarbonization.

Optimized Dual-Layer Distributed Energy Storage Configuration for Voltage Over-Limit Zoning Governance in Distribution Networks

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network’s voltage zones based on distributed photovoltaic governance resources, thereby elucidating the characteristics and governance requisites for voltages across distinct regions. Subsequently, a governance model for voltage limit exceedances, grounded in optimizing energy storage configurations, was formulated to mitigate photovoltaic power fluctuations by deploying energy storage systems. This model coordinates the reactive power output of photovoltaic installations with the active power consumption of energy storage systems, thereby augmenting voltage autonomy in the power grid. This study leveraged Karush–Kuhn–Tucker (KKT) conditions and the Big-M method to transform the dual-layer model into a single-layer linear model, thereby enhancing solution efficiency and precision. Finally, a simulation was carried out to demonstrate that the strategy proposed from this research not only achieves commendable economic efficiency, but also significantly improves the regional voltage effect by 28.7% compared to the optical storage capacity optimization model.

Fault mitigation mechanism to pave the way to accommodate over 90% renewable energy in electric power systems

Renewable energy plays a key role in reducing carbon emissions. The electric power system is widely considered a platform to accommodate renewable energy because of the high efficiency in transmitting energy through electricity tie lines. However, most renewable energy generation is weather-dependent, bringing difficulties in balancing generations and electricity demands. There are two major solutions to address the imbalance problem. The first is to balance the fluctuation of renewable energy in time scales by deploying energy storage, but with huge costs and security risks. The other is to balance the fluctuation in spatial scales by enhancing the interconnection among electric power systems, which however results in a sharp increase in the fault current in many countries. The high level of fault current threatens the security of power system. In other words, the overrating fault current of electric power systems is one of the key bottlenecks for carbon neutrality. To mitigate the high-level fault current because of the interconnection, we propose a novel mechanism, namely fault current release. It achieves new functionalities with proven equipment at low cost and will create no impact on the electric power systems in normal operation. The simulation selected six representative electric power systems, including those in the USA, the UK, China, Brazil, Nigeria and IEEE reliable test system. Results show that the fault current release method enables an increase of renewable energy accommodation from 19.2% ∼ 47.2% to over 90%, much higher than the existing methods can do. Correspondingly, the emissions of CO2, NOX and SOX in these systems decrease by 67.84% ∼ 88.07%, 61.17% ∼ 88.24% and 58.25% ∼ 88.33%, respectively. The proposed method is also economical in that it can accommodate renewable energy at about one-tenth the cost of energy storage. Our findings hence suggest a promising way to build a carbon-free energy system.

A distributionally robust optimization approach for optimal load dispatch of energy hub considering multiple energy storage units and demand response programs

The coupling relationship among various energy sources has been consistently reinforced by the advancement of the energy Internet. In the context of modeling multi-energy systems, the energy hub (EH) represents a significant and valuable approach. To attain optimal operation of the EH, this study proposes an optimal load dispatch model for a system that incorporates a combined heat and power (CHP) unit, gas boiler (GB), electric chiller (EC), absorption chiller (AC), heat energy storage (HES) unit, and electrical energy storage (EES) unit. A two-stage distributionally robust optimization (DRO) method that is driven by data is employed to address the uncertainties of electricity prices. Additionally, the proposed two-stage DRO model is effectively solved through the utilization of the column and constraint generation (C&CG) algorithm. Three cases are discussed in the paper with various energy storage units and demand response (DR) settings. The simulation results show that by deploying energy storage units and participating in DR projects, the EH system can reduce the total costs by 2.56 % and 10 %, respectively. Meanwhile, simulation results reveal that the proposed data-driven DRO model can achieve a compromise between the economy and robustness of the scheduling model compared with stochastic optimization (SO) and robust optimization (RO) methods. The simulation results illustrate the effectiveness of the proposed model for ensuring the economical and efficient operation of the system in the face of electricity price uncertainties.

Economic Viability Assessment of Neighbourhood versus Residential Batteries: Insights from an Australian Case Study

Amidst the evolving paradigms of the contemporary energy landscape, marked by the imperative of sustainability and efficiency, the integration of energy storage has emerged as a transformative strategy that seeks to recalibrate the dynamics of electricity distribution and consumption. However, there remains a pressing need to determine the most economically viable approach for deploying energy storage solutions in residential low-voltage (LV) feeders, especially in rural areas. In this context, this paper presents the results of an economic evaluation of energy storage solutions for a residential LV feeder in a rural town in Australia. Specifically, the study compares the financial viability of a front-of-the-meter (FTM) battery installed on the feeder with that of a fleet of behind-the-meter (BTM) batteries. The FTM battery, with a size of 100 kW/200 kWh, is assumed to be operated by the retailer but owned by the community, with any profits assigned to the community. In this scenario, we studied a battery operating under standard network tariffs and three different trial tariffs that distribution network service providers currently offer in Australia. On the other hand, the fleet of BTM batteries (3 kW, 3.3 kWh) are individually owned by households with solar installations, and their cumulative capacity matches that of the FTM battery. The comparison is based on key economic parameters, including network charges, retail margins, frequency control ancillary service (FCAS) revenues, wholesale energy costs, technology costs associated with community batteries, and net profit or loss for the community, as well as considerations of utility grid arbitrage and solar photovoltaic (PV) self-consumption. The study also assumes different grant levels to assess the impact of subsidies on the economic feasibility for both battery configurations. The findings indicate that, while both require some form of subsidy for profitability, the BTM batteries outperform the FTM battery in terms of economic viability and so would require lower grant support. The FTM battery case finds a need for grants ranging from 75% to 95% to break even, while the BTM fleet requires approximately 50% in grants to achieve a similar outcome. In conclusion, this study highlights the importance of grant support in making energy storage solutions economically feasible. In particular, it highlights how the less mature segment of FTM batteries will need higher support initially if it is to compete with BTM. The outcomes of this study inform decision-making processes for implementing energy storage solutions in similar communities, fostering sustainable and cost-effective energy systems.

The economic impact of location on a solar farm co-located with energy storage

Deploying energy storage (ES), alongside renewable generation, can help to decarbonise electricity grids. A key aspect of deploying these is choosing a suitable location, which is both geographically feasible and economical. Previous studies identify locations with suitable geographies; here we focus on the economic impact of location. We explore how the maximised profits, determined using a mixed integer linear programming (MILP) optimisation model, of a solar farm co-located with ES vary in different regions around Great Britain (GB) as a case study. We perform a cost–benefit analysis from the point of view of a distribution-connected solar farm owner. Real solar generation data is used, along with a weather model, to accurately represent forecast and actual output. For solar farms without ES profits are higher in locations with greater solar irradiance. However for sites with ES we find greater profit variation, primarily due to different distribution charges. For the majority of GB, ES does not add sufficient value to offset its high upfront costs and is not worth adding to solar sites. Additionally, it is found to be uneconomical to add ES to most existing solar farms, despite many studies highlighting the grid benefits this would bring. We recommend that distribution network operator and market pricing better reflects the value which ES can bring to the electricity system economical to add to solar sites. To encourage increased co-location distribution operators should offer greater a differential between non-intermittent generation and intermittent generation payments, in particular at times of high system demand.

Solar business prosumers in Ukraine: Should we wait for them to appear?

The prosumerism involving self-consuming green energy generated by small power producers significantly contributes to energy transition and sustainable development. Its practical implementation requires creating strong incentives for businesses and households. In Ukraine's example, this study covers a gap in substantiating business solar prosumerism in a transition economy. We used statistical and comparative analyses to assess the necessary legal and organizational conditions for developing the solar power sector and confirmed its positive outcomes. We applied investment analysis to substantiate the economic feasibility of converting industrial green energy producers to prosumers. The net present value, profitability index, and discounted payback period of constructing industrial photovoltaic solar power plants with 100, 300, 500, and 700-kW capacity were evaluated. Three options for the ratio “green electricity self-consumed/sold at a feed-in tariff” were chosen: 1) 20/80; 2) 50/50; 3) 100/0. We found that pure self-consumption was the most profitable and boosted creating the business solar prosumer class. Using multifactor and comparative analyses, we identified the prospective post-war factors of prosumerism, including electricity prices, green energy sales taxation, investment incentives, etc. The policy recommendations of the study contain the business feed-in tariff cancellation, investment support for deploying energy storage systems and prosumer microgrids.

Power System Flexibility Enhancement with Energy Storages Considering Charging Characteristics

With the increasing integration of renewable energy generations, the renewables’ uncertainties impact the power system operation. So, it is critical to enhancing the system’s operational flexibility. To this end, one of the most useful solutions is to deploy energy storage. This paper proposes a power system flexibility enhancement method using energy storage systems (ESSs). The grid’s flexibility requirement is analyzed, and the flexibility enhancement model is constructed, considering the ESSs’ charging and discharging characteristics. Case studies on an IEEE 33-node system verify the quantitative method. The conclusions indicate the ESSs could efficiently meet the system’s flexibility requirements.

Multi-timescale capacity configuration optimization of energy storage equipment in power plant-carbon capture system

Deploying energy storage technologies into power plant-carbon capture systems has received much attention since it can greatly improve the flexibility of the plant, thus enhancing the competitiveness in the electricity market. Determining the reasonable capacity of the energy storage equipment is the key to ensuring a reliable, economic, flexible and low carbon operation of the entire plant. To this end, this paper proposes a multi-timescale capacity configuration optimization approach for the deployment of energy storage equipment in the power plant-carbon capture system, in which both the long-timescale economic, emission, peak loading shifting performance, and the short-timescale load ramping performance are evaluated through the joint use of the steady-state and closed-loop dynamic plant models. Case study on the capacity configuration of the molten-salt heat storage equipment in the power plant-carbon capture system shows that the proposed multi-timescale capacity configuration optimization approach can reduce the totalized costs by 2.15% compared with the conventional capacity configuration approach. Other energy storage technologies such as battery and lean/rich solvent storage are also optimized and compared under different electric market conditions to provide broader insights on their roles in the power plant-carbon capture system.

A Two-stage Stochastic Mixed-integer Programming Model for Resilience Enhancement of Active Distribution Networks

Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks (ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming (SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems (ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm (PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.

Use of Energy Storage to Reduce Transmission Losses in Meshed Power Distribution Networks

One of the challenges which the electrical power industry has been facing nowadays is the adaptation of the power system to the energy transition which has been taking place before our very eyes. With the increasing share of Renewable Energy Sources (RES) in energy production, the development of electromobility and the increasing environmental awareness of the society, the power system must constantly evolve to meet its expectations regarding a reliable electricity supply. This paper presents the issue of deploying energy storage facilities in the meshed power distribution network in order to reduce transmission losses. The presented multi-objective approach provides an opportunity to solve this issue using multi-objective optimisation methods such as Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multiobjective Particle Swarm Optimization (MPSO) and Biased Random Keys Genetic Algorithm (BRKGA). In order to increase the efficiency optimisation process, the Pareto Adaptive ϵ-dominance (paϵ-dominance) was used. It was demonstrated that the use of energy storages that cooperate with RES can significantly reduce transmission losses.

Planning urban energy systems adapting to extreme weather

• Urban energy systems are planned considering extreme weather events (EWE). • A flexible and efficient two-layer framework is developed with generality. • Increasing supply or deploying storage can ensure energy supply during EWE. • ‘Storage Prior’ heuristics requires 2.8% extra cost for deploying storage. • ‘Supply Prior’ heuristics needs 5.4% extra cost for expanding gas power capacity. In the context of increasing urbanization and climate change globally, urban energy systems (UES) planning needs adequate consideration of climate change, particularly to ensure energy supply during extreme weather events (EWE) such as heatwaves, floods, and typhoons. Here we propose a two-layer modeling framework for UES planning considering the impact of EWE. An application of the framework to a typical coastal city of Xiamen, China reveals that deploying energy storage (i.e., pumped hydro and battery) offers significant flexibility to ensure the critical demand is met during typhoon as a typical EWE and avoids over investment in supply technologies. This requires an extra 2.8% total cost on investment and operation of UES for 20 years. Planning energy systems with proper consideration of EWE can ensure robust urban energy services even with increasing penetration of fluctuating renewables, and we offer a flexible and computationally efficient paradigm for UES planning considering the impact of EWE.

Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty

The importance of energy efficiency and demand response management while harnessing renewable energy draws more attention from many industries in recent years. Seaports, as large scale end-users, aim to adopt energy management systems (EMS) since energy prices have increased over years and sustainable operations is a key target for greening the port industry. Many seaports start to install fully electrified equipment and use electricity as the source of energy because electricity consumption, instead of carbon-intensive energy sources, contributes to the climate change mitigation targets. In this study, a mixed integer linear programming model is suggested to solve the integrated operations planning and energy management problem for seaports with smart grid (e.g. port microgrid) considering uncertain renewable energy generation. The operations planning aims to determine the number of quay cranes (QCs) and yard equipment to assign to each ship for each one hour period. It also determines each ship’s berthing duration which affects the hourly energy consumption due to the cold ironing and the available reefer containers. These plans result in energy demand. Meanwhile, energy management matches energy demand and supply considering different energy pricing schemes and bidirectional energy trading between energy sources (e.g. utility grid, renewable energy sources) and energy storage systems. Results indicate that significant cost savings can be achieved with smart grid (port microgrid) compared to conventional settings. Deploying energy storage systems in port microgrid results in important cost savings. Energy consumption is dominated by QCs, cold-ironing and reefer containers. Finally ports which harness renewable energy obtain significant costs savings on total cost.

Energy storage usages: Engineering reactions,economic‐technologicalvalues for electric vehicles—A technological outlook

At present with the massive induction of distributed renewable energy sources (RES), energy storage systems (ESS) have the potential to curb the intermittent nature of micro sources and provide a steady supply of power to the load. It gives an optimum solution and considers as a major part of intelligent grids. For making a green environment, Electric Vehicle (EV) is the best option that emits zero exhaust gases, cleaner, less noisy and eco-friendly compared to engine-based vehicles. It could embark power sanctuary by allowing open access to RES. Nonetheless, EVs presently face encounters in the deployment of ESSs, inroad to their reliability, capacity, price, and online management issues. This study comprehensive review about technical advancements of ESSs, its detailed taxonomy, features, implementation, possibilities with system differences, and additional features of particularly EV applications. Hence, in this current study, technical analysis of Energy storage systems, its leading technologies, core assets, global energy stakeholders, economic merits and techniques on energy conversion is provided. Besides, the way of deploying energy storage techniques, the barriers and assessments are also presented to give a wider scope in this particular area.

Measuring the value of energy storage systems in a power network

The increased use of renewable generators and their intermittent behavior motivates network operators to deploy energy storage systems. In this study, energy storage types, locations, and capacities are optimized for a capacitated electric power network with renewable generation. Short term operational decisions that include charging/discharging schedules and capacity management of the storage systems are included in this optimization framework to capture hourly, daily, and seasonal fluctuations of the demand, renewable generation, and energy prices. A Mixed Integer Linear Programming (MILP) formulation is developed but because of the computational complexity, a mathematical programming based metaheuristic algorithm is proposed. With a numerical study, the proposed heuristic method is proved to be highly effective compared to the MILP formulation and an existing state of the art algorithm. The effects of storage installation costs, line capacities, demand and generation variance on the values of storage systems and on the installation decisions are analyzed through numerical studies.