Cloud Energy Storage Research Articles

A novel and cost-effective model for cloud energy storage based on a dual storage setup in active distribution grid with resilience consideration

The rising occurrences of natural disasters, terrorist actions, and cyber-attacks that result in extensive, long-lasting, and expensive disruptions have necessitated a shift in focus towards the resilience of electrical grids for network operators. However, the task of designing a resilient network remains complex and costly. One potential solution to bolster resilience is the deployment of battery energy storage devices on the consumer side, known as distributed energy systems (DES). Despite its effectiveness, the high construction costs and lengthy payback period associated with investing in energy storage devices have led consumers to exhibit reluctance in adopting them. Cloud energy storage (CES) is an innovative and cost-effective solution to address those challenges. In the CES platform, investors install storage facilities in the network which can be rented by consumers to fulfill their needs and they become holders of the virtual batteries. By adopting this approach, consumers are relieved from the burden of maintenance, repair, and installation. While a single CES facility offers reduced costs and increased comfort for consumers, it compromises the resilience of the grid when compared to the Distributed Energy Storage (DES) mechanism. In order to bridge this gap, this paper proposes a dual CES model which serves as an intermediate solution between DES and single CES. The dual CES model strikes a balance between the resilience of the grid and cost-effectiveness. It provides a higher level of resilience compared to a single CES and a lower level compared to DES. Additionally, the costs associated with the dual CES model fall between that of a single CES and DES. This model not only increases the profit margin for investors but also enhances the overall comfort and well-being of consumers compared to the single CES. To validate the proposed model, a Mixed Integer Linear Programming (MILP) problem is formulated and simulated on the IEEE 33 bus network. Three cases are considered: DES, single CES, and dual CES. The results indicate that the dual CES reduces consumers' costs by 28 %, losses by 27 %, unsupplied load costs by 45 %, and return on investment by 33 %. Moreover, it increases the stability margin time by 2 intervals and improves robustness by 47 %.

UNDERSTANDING THE ROLE OF V2G TECHNOLOGY ON GRID STABILITY DURING PEAK HOURS IN INDIA

India has set an ambitious goal of becoming Net Zero in carbon emissions by 2070. To achieve this goal there are significant steps being taken especially in the power and electric vehicle sector. A revolution that involves symbiosis of both the sectors, is the integration of V2G (Vehicle to Grid) technology in India. This paper gives an overview of power sector and electric vehicles market in India followed by need, benefits, and infrastructure required for bringing V2G in India. It also includes theinsights froman interview with the founder of SHERU, a start-up which provides cloud energy storage and is working closely with the related concepts. The methodology to calculate crude oil saving, C02 emission, journey cost, public charging and home charging is also discussed. The paper looks into the case studies of few developed countries that have been using V2G and emphasizes on how this technology can help to achieve sustainability goals.

Optimal scheduling model using the IGDT method for park integrated energy systems considering P2G–CCS and cloud energy storage

To enhance the energy efficiency and financial gains of the park integrated energy system (PIES). This paper constructs a bi-level optimization model of PIES-cloud energy storage (CES) based on source-load uncertainty. Firstly, the scheduling framework of PIES with refined power-to-gas (P2G), carbon capture and storage (CCS) and CES coupling is constructed. Moreover, a bi-level optimization model with the upper tier subject being the PIES operator and the lower tier subject being the CES operator is established under the ladder-type carbon price mechanism with reward and punishment (LCPMRP). Then a proposed entropy weight adaptive information gap decision theory method (EAIGDT) is proposed to eliminate the subjectivity factor and retain its non-probabilistic features while dealing with multiple source-load uncertainties, and according to the operator’s risk preference to build risk-averse (RA) and risk-seeking (RS) strategies, respectively. Finally, the measured data in a certain area of Xinjiang verifies the proposed optimal scheduling method. The results show that the method can effectively take into account the interests of various subjects and realise PIES low-carbon economic operation.

Blockchain-enabled framework for transactive home energy management with cloud energy storage under uncertainties

In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid’s delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling.

Data driven net load uncertainty quantification for cloud energy storage management in residential microgrid

Data driven net load uncertainty quantification for cloud energy storage management in residential microgrid

Optimized scheduling study of user side energy storage in cloud energy storage model.

With the new round of power system reform, energy storage, as a part of power system frequency regulation and peaking, is an indispensable part of the reform. Among them, user-side small energy storage devices have the advantages of small size, flexible use and convenient application, but present decentralized characteristics in space. Therefore, the optimal allocation of small energy storage resources and the reduction of operating costs are urgent problems to be solved. In this study, the author introduced the concept of cloud energy storage and proposed a system architecture and operational model based on the deployment characteristics of user-side energy storage devices. Additionally, a cluster scheduling matching strategy was designed for small energy storage devices in cloud energy storage mode, utilizing dynamic information of power demand, real-time quotations, and supply at the load side. Subsequently, numerical analysis was conducted to verify that the proposed operational mode and optimal scheduling scheme ensured the maximum absorption of renewable energy, improved the utilization rate of energy storage resources at the user side, and contributed to peak shaving and load leveling in the power grid. The model put forward in this study represents a valuable exploration for new scenarios in energy storage application.

Stochastic optimal transactive energy management with cloud energy storage using artificial neural networks

AbstractResidential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers’ costs in the proposed model.

Distributed risk-constrained strategy management of cloud energy storage for residential and electricity market services provision

Energy storage systems can improve the performance of home energy management systems and also provide ancillary services in electricity markets. However, the economic and technical insufficiency of individual home storage units, makes employing them inefficient for domestic storage and market applications, respectively. Therefore, central storage units with capacity sharing capability to small consumers as Cloud Energy Storages (CESs) are becoming a promising solution, and are also potentially able to contribute to ancillary service provision in the market. In this context, this paper aims to develop a novel stochastic day-ahead distributed management framework for CES to simultaneously adopt the optimal strategy of storage allocation for photovoltaic-integrated homes and participation in energy and ancillary service markets. The developed distributed framework is formulated within the Benders decomposition approach in which CES strategy and home energy management systems are optimized through master and subproblems based on linear mathematical models, respectively. Furthermore, the uncertainty of market prices is handled by the Conditional Value at Risk (CVaR) method. The developed model is assessed through real-world case studies of ERCOT. The results highlight the effectiveness of the collaborative service framework for both CES and households. Energy arbitrage through local storage services compensates for CES's challenges such as degradation and low power rating in the provision of energy services. These improvements benefit CES owners with higher net revenues and households with reduced cost of electricity supply.

Uncertainty aware optimal battery sizing for cloud energy storage in community microgrid

Cloud energy storage systems (CES) are a new paradigm for the application of consumer-side energy storage in residential community microgrids. By transforming traditional consumers into self-sustaining and utility consumers, CES facilitates interaction between consumers and utilities as well as between consumers. Residential CES development is complicated by the flexible capacity of CES batteries and the uncertainty of supply. This paper recommends the CES system for residential prosumers. A machine learning-based uncertainty quantization and an artificial ecosystem optimization (AEO) method are used to determine optimal battery capacity considering PV, load, and price uncertainty. Compare this algorithm to the other four optimization algorithms to find out how well it works. The feasibility and profitability of deploying CES with residential PV are assessed. This problem minimizes the user’s cost and maximizes the profit of the CES operator. The sensitivity analysis of CES is analyzed for various storage capacity penetrations. Further, optimal battery capacity determines the use of an AEO algorithm. Based on a PJM dataset of residential PV, load, and electricity price, simulation results demonstrate that the suggested framework integrated with a distributed PV system is more economical to DES.

Two-Stage Capacity Determination Framework for Residential Second-Life BESSs Considering Cloud Energy Storage Service

Second-life battery energy storage systems (SL-BESSs) have potential to be used as an economic and affordable energy storage solution for supporting a variety of applications, such as energy backup in residential sector. This article proposes a new capacity determination framework for SL-BESSs that are utilized in household environment. Despite the capital cost is significantly reduced, the degradation characteristics of retired batteries exhibits two-stage decline characteristics. The proposed framework first establishes the SL-BESSs model and estimates its remaining useful lifetime by identifying the “knee point” in the SL-BESSs capacity degradation curve; based on this, a two-level SL-BESS capacity determination model is formulated, which sizes the SL-BESS based on short-term home energy management under different scenarios. In particular, this article considers the dual-service modes of an SL-BESS in its capacity determination process: first, provide local energy supply to the household, and second, provide cloud energy storage service to external users. Extensive numerical simulations are conducted to validate the proposed framework.

Optimal planning of energy storage system under the business model of cloud energy storage considering system inertia support and the electricity-heat coordination

As the penetration rate of renewable energy increases in the electric power system, the issues of renewable power curtailment and system inertia shortage become more severe. Innovative solutions such as Cloud Energy Storage (CES) can be employed to address this challenge. However, the energy storage resources aggregated by the traditional CES business model mainly concentrate on Electrical Energy Storage (EES), which is still limited and expensive. It necessitates the exploration of new approaches to enhance the flexibility and cost-effectiveness of energy storage utilization, in which using District Heating System (DHS) as an equivalent energy storage resource of the power system is an effective method. Therefore, this paper proposes an optimal planning strategy of energy storage system under the CES model considering inertia support and electricity-heat coordination. Firstly, the system components and business model of the CES are described, and the framework of energy storage planning problem from the perspective of CES operator is formulated. Then the evaluation methods of energy storage utilization demand from CES users are proposed, including the evaluation of the renewable power curtailment, system minimum inertia requirement, and the equivalent energy storage ability of DHS. Based on this evaluation results, a bi-layer optimal energy storage planning model for the CES operator is established, where the upper-layer model determines the installed capacity of lithium (Li-ion) battery station and the lower-layer model determines the optimal schedules of the CES system. The numerical tests based on the operation profile of a typical city in China are carried out to demonstrate the effectiveness of the proposed method. The simulation result shows that the annual profit of the CES system can be improved by 15.26% after installing the energy storage system whose capacity is determined by the proposed method.

Multi-Stage Proactive Scheduling of Strategic DISCOs in Mutual Interaction With Cloud Energy Storage and Deferrable Loads

Unexpected events have underscored the need for proactive preparedness in local energy systems, especially strategic distribution companies (DISCOs). The state-of-the-art methods to take proactive approaches in DISCOs are to use battery energy storage systems (BESS). However, there is a lack of established approaches that include the inter-dependencies of techno-socio economic features. To overcome this limitation, this paper constitutes a multi-stage resilience-promoting proactive strategy with linear decisions to enhance the strategic DISCOs preparedness level to deal with unscheduled islanding operation under uncertainties. In particular, this strategy provides operational solutions to survive DISCOs under stressful conditions by incorporating the economic operation of cloud energy storage (CES), which manages many BESS, and correlated demand response (DR) programs in resiliency issues. To this end, the lexicography algorithm is employed to prioritize different objectives based on the pre-avoidance, avoidance, survival, and post-disaster stages, which captures the BESS' energy level as well as socio-economic features of DISCO and CES. The effectiveness of the proposed strategy is demonstrated both analytically and empirically. At first, the features of the proposed strategy are examined in responding to islanding state using a set of numerical studies conducted on the IEEE 69-bus test system in GAMS software. After that, insightful analyses are performed via DIgSILENT PowerFactory to verify the feasibility of the proposed strategy.

A collaborative management strategy for multi-objective optimization of sustainable distributed energy system considering cloud energy storage

The hybrid renewable energy distributed energy system (DES) has great application potential due to its cleanliness and high energy efficiency. In existing DES studies, complex energy storage systems are invested in addressing the mismatch between energy supply and demand. However, this investment mode would result in large investments and low device utilization rates. This work connects DES to cloud energy storage (CES, a shared energy storage provider) to build the DES storage system in a subscription mode. The subscription mode enables the proposed DES-CES to dynamically adjust its storage capacity and power by changing storage service contracts. Then, an active energy reserve-based multi-stage dual-scenario collaborative management strategy is designed to dispatch DES-CES. A multi-objective optimization model is constructed to optimize the design and operation of DES-CES, considering economic, environmental, and flexibility performances. Simulation results show that the formulated three performances form a sustainability frontier for DES-CES. This frontier has suitable renewable energy integration levels that consider system consumption ability. Compared to the investment mode, the annual equivalent storage capacity and power under the proposed subscription mode are less, reducing storage costs by 13%–53% (depending on given performances). This work provides references and methodology for DES to join CES.

Optimal operation of distribution networks and multiple community energy prosumers based on mixed game theory

Community energy systems are currently transitioning from conventional consumers to prosumers. Under this context, the coordinated management of multiple community energy prosumers (CEPs) is explored this study in accordance with the same distribution network. The aim of this study is to facilitate the coordination of unit operations, demand response, and peer-to-peer (P2P) energy trading among CEP coalitions based on the distribution network operator (DSO) by setting electricity prices. Thus, a mixed game-based optimal operation model of DSO and CEP alliances is developed, integrating Stackelberg and cooperative games. The upper layer aims at maximizing DSO revenue, whereas the lower layer minimizes the operation costs of CEP cooperative alliances while allocating benefits in accordance with the Nash-Harsanyi bargaining theory. The model is solved with a two-stage distributed algorithm that integrates the bisection method and the alternating direction method of multipliers (ADMM). Furthermore, cloud energy storage (CES) is introduced to optimize the economics of CEP. The effectiveness of the proposed method is verified through a case study, demonstrating the collaborative optimal scheduling of multiple CEPs and reasonable benefit distribution. Accordingly, this study is conducive to expediting the effective and economical management of CEPs under distribution networks.

Optimal allocation of cloud energy storage system in low-voltage distribution network

In low-voltage distribution networks, an uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) brings about intensive peak loads in distribution transformers’ daily load curves. Due to insulation degradations, this issue ends in transformers’ loss of life (LOL) and early aging. The notion of cloud energy storage system (CESS) with larger power and energy capacities enables consumers to have access to cheaper energy storage facilities. Thanks to CESS installation, semi-smart, controlled, and low-cost charging of PHEVs could be realized to relieve the transformer’s peak loads and reduce the peak-to-average (PAR) ratio of load curve. To do so, this paper develops an optimal planning model for siting and sizing of a CESS to accommodate the charge/discharge requirements of consumers who are willing to collaborate with CESS and hence, implementing a controlled charging of PHEVs. The proposed model considers the investment and operation costs of both CESS and distribution network. It also models the transformer LOL costs. The PHEVs, in private garages, are supposed to be connected to the grid through a smart module whose connection time and status is optimally scheduled and controlled through the operator of the CESS. The CESS charging/discharging is regulated such that the previously determined time targets of PHEV owners are satisfied. To assess performance of the proposed model, extensive numerical studies are carried out in terms of techno-economic indices say as PAR reduction, conducting energy arbitrage, power losses reduction, and etc. Results are discussed in depth.

Cloud energy storage in power systems: Concept, applications, and technical challenges

AbstractCloud energy storage (CES) in the power systems is a novel idea for the consumers to get rid of the expensive distributed energy storages (DESs) and to move to using a cloud service centre as a virtual capacity. Although the different characteristics and applications of the energy storages are reviewed in some papers, there is no review study on the CES concepts, formulations, applications, and challenges. Therefore, the main contribution of this paper is to review the applications of the CES and its technical challenges in the power systems. For this purpose, the concept and fundamentals of the CES, as well as their role in supporting the consumers and the power network, are described first. The flow of information in a CES is then discussed, and the roles of the operator, consumers, and facilities, as the main sectors of the CES are explained. The existing studies are classified and discussed regarding the different applications of the CES in the power systems and their drawbacks are highlighted. The operation and planning (feasibility) problems of the CES are investigated. Reviewing the existing studies shows that comprehensive models are required to address the energy management (EM) and feasibility analysis of the CES applications. To address this challenge, the general formulations are presented for the planning and the operation scheduling problems of the CES. In addition, addressing different CES applications in the power systems leads to some technical challenges which are described. Finally, future directions are suggested for potential researchers to continue the studies on the CES integration and application.

Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market

Cloud energy storage (CES) receives increasing attention as an efficient and viable paradigm for the provision of distributed energy storage services. This paper exploits CES’s service modes to both energy storage and electricity trading for its users, e.g., microgrid (MG). The optimal day-ahead bidding strategy is investigated for CES as an independent entity in the electricity market. Besides, two energy service modes are introduced considering MG's requirements and preferences. Each mode consists of a set of schemes for energy storage system (ESS) rent (power-based and capacity-based) and electricity trading (internal price with an improved pricing method and market clearing price). Finally, a stochastic programming (SP)-based optimization model is formulated to maximize the CES’s expected profits fully considering the electricity market’s settlement mechanism and uncertainty as well as the participation interest of MG. The proposed solution is extensively assessed through a case study of CES’s energy services to five heterogeneous MGs with distinct electricity generation and consumption characteristics and two energy service modes are assessed through comparative experiments. The numerical results confirm the effectiveness and benefits of the proposed optimal day-ahead bidding solution.

Relaxation‐based bi‐lever reformulation and decomposition algorithm‐based collaborative optimization of multi‐microgrid for cloud energy storage

AbstractEnergy storage devices become an indispensable part of modern power systems with high renewable energy penetration level. To reduce the operating costs, it is a promising way to allow the sharing and leasing of energy storage devices. In this paper, a bi‐lever optimized dispatch scheme is proposed to improve the usage efficiency of cloud energy storage in multi microgrids (MMG) system. Minimizing the operating costs of shared cloud energy storage is the main task of the upper lever while maximizing the profits of MMG is the goal of the lower lever. Moreover, the transaction cost and benefit between the two levers play an important role in system level optimization. This leads to a hybrid optimization problem with both discrete decision variables and continuous decision variables. To solve the problem, a relaxation‐based bi‐lever reformulation and decomposition algorithm is developed. The effectiveness of the proposed bi‐lever dispatch optimization model is verified by carrying out numerical experiments in three scenarios. It is shown that the proposed cloud energy storage service can effectively reduce the operating cost of MMG.

Guest editorial: Application of cloud energy storage systems in power systems

Guest editorial: Application of cloud energy storage systems in power systems

The Cost Efficiency of the Electricity Retailers with the Integration of the Cloud Energy Storage

As a result of market liberalisation, a large number of electricity retailers have emerged in the electricity market. Acting as the intermediaries between the electricity producers and the customers, the electricity retailers aim to balance the supply and demand and shoulder substantial risks generated from both sides. Due to the randomness of the electricity load, it is difficult for electricity retailers to make an accurate electricity purchasing plan in advance to meet customer demand. This deviation leads to a proportion of spot electricity purchases that require a higher purchase cost. As a result, one of the most serious concerns facing electricity retailers is how to improve their balancing abilities and reduce power purchase deviation. In contrast to previous research, which has generally recommended that electricity retailers invest in energy storage systems or develop optimised purchasing strategies, this paper proposes a new strategy for the electricity retailers, which is renting external flexible resources to solve the market uncertainty of the electricity retailers, thereby lowering purchase costs and increasing profits. The proposed business model makes use of the cloud energy storage to solve the supply-demand imbalance issue of electricity retailers. The cost calculation model and decision optimisation model have been established in the process of renting cloud energy storage. Charging and discharging cloud energy storage have been separately rented to deal with different positive and negative load deviations, which can simplify the optimisation model. As an experimental paper, the proposed model has been tested in the PJM power market in the United States and the New South Wales power market in Australia. The findings confirm that renting the cloud energy storage capacities can significantly reduce costs and maximise profits for the electricity retailers when compared to the situation without the cloud energy storage. The biggest saving can reach 24.5% in the PJM market. With the rapid fall of battery prices, the advantage of the proposed strategy will be more obvious.