Optimal Storage Allocation Research Articles

Optimal Energy Storage Allocation for Combined Wind-PV-EVs-ES System Based on Improved Triangulation Topology Aggregation Optimizer

To determine the ES allocation based on a specific number of EVs connected to a combined WPESS, this paper develops an ESS allocation model that considers the impact of EV charging behavior on LSD, ES allocation cost, new energy utilization rate, and self-power rate. First, several scenarios are generated using Monte Carlo sampling (MCS), and a typical day is selected through Backward Reduction (BR). Next, the Monte Carlo method is employed to generate conventional EV charging curves and optimize EV charging behavior by considering LSD and user charging costs. Subsequently, an ES capacity allocation model is developed, considering system costs, new energy utilization rate, and self-power rate. Finally, an improved triangulation topology aggregation optimizer (TTAO) is proposed, incorporating the logistic map, Golden Sine Algorithm (Gold-SA) strategy, and lens inverse imaging learning strategy. These enhancements improve the algorithm’s ability to identify global optimal solutions and facilitate its escape from local optima, significantly enhancing the optimization effectiveness of TTAO. The analysis of the calculation example indicates that after optimizing the charging behavior of EVs, the average daily cost is reduced by 204.94, the self-power rate increases by 2.25%, and the utilization rate of new energy sources rises by 2.50%, all while maintaining the same ES capacity.

An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations.

A modified Runge–Kutta optimization for optimal photovoltaic and battery storage allocation under uncertainty and load variation

The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Bi-level Planning Model for Optimal Battery Energy Storage Allocation Considering Optimal Daily Scheduling Using Mixed-Integer Particle Swarm Optimization

Bi-level Planning Model for Optimal Battery Energy Storage Allocation Considering Optimal Daily Scheduling Using Mixed-Integer Particle Swarm Optimization

Energy Storage Configuration Method for Low-Voltage Distribution Stations Taking Into Account Economy and Power Supply Reliability

To address the reliance on fossil fuels, a significant number of photovoltaic systems have been integrated into the power grid. This has led to a situation where the locally generated photovoltaic power cannot be consumed on-site, causing an increase in the voltage of the distribution network and potentially exceeding limits. Additionally, this results in an increase in network losses within the distribution network. Energy storage systems have a high degree of versatility and can be utilized to address the issue of misalignment between solar panel output and energy demand due to grid-connection of photovoltaic systems. This is achieved through the system’s quick charging and discharge capabilities, and its ability to control the charging and discharge state.This paper presents models for optimal energy storage allocation in both normal operations and failure situations. It outlines a process for allocating energy storage that balances economic considerations with power supply reliability. To illustrate the effectiveness of the energy storage system in enhancing the distribution network, a practical example is provided in a substation, both under normal and fault conditions.

Backhaul-Aware Storage Allocation and Pricing Mechanism for RSU-Based Caching Networks

Remarkable prevalence of in-car entertainment systems empowers vehicular users to download multimedia-enabled contents in transit and creates a new business opportunity for content providers (CPs). However, the timely delivery of requested content is a major concern for CPs to improve the quality of service (QoS) of their subscribe users. Roadside unit (RSU)-based caching appears as a promising solution for CPs wherein CPs proactively store their content at the RSU to reduce the content delivery time. Since the RSUs are enabled with limited storage capacity, the competition among multiple CPs for storage space is unavoidable. Further, the CPs are connected with RSUs using capacity limited backhaul links. Hence the allocation of RSUs’ storage among CPs becomes a fundamental issue in RSU-based caching networks. In this paper, we design a market scenario in which the set of CPs competes for the storage space of RSUs. In the unavailability of utility and cost functions of the CPs and the RSUs, we introduce a market maker to manage the marketplace. Further, we employ iteration-based double-sided auction mechanism to compute the optimal storage allocation and corresponding payment transfer for CPs which maximizes the social welfare of the networks. The simulation results demonstrate the proposed auction mechanism improves the social welfare of the network by at least 29.3% compared to the benchmark schemes. Further, with the help of both analytical and numerical analysis, we show that the proposed auction mechanism also holds vital economical properties.

Optimal Energy Storage Allocation Strategy by Coordinating Electric Vehicles Participating in Auxiliary Service Market

The further liberalization of China's electricity market encourages demand-side entities to participate in electricity market transactions. Electric vehicles (EVs) are developing rapidly and have high regulating potential, and are the main force for demand-side participation in the auxiliary service market. Aiming at the problems of dispatching accuracy and economy in EV participation in auxiliary service market, this paper analyzes the bidding strategy and dispatching scheme of EV-storage participation in auxiliary service market, and proposes EV-storage optimal allocation strategy with the goal of economic optimum. In the process of optimal allocation, based on the market rules of third-party subject participation in auxiliary services, the bidding strategy of EV-storage coordinated EV participation in auxiliary services market considering daily load scale changes is designed, while the conditional value at risk (CVaR) method is used to determine the short-term coordinated energy storage capacity and efficiency storage capacity considering the uncertainty of EV response situation; then, based on the annual EV load scale change, the benefit calculation function is constructed by considering various factors such as auxiliary service market revenue, spread revenue, investment cost and market opportunity cost of EV-storage participation in the auxiliary market. Finally, taking EV aggregation participation in the valley-filling ancillary service market as an example, it is verified that the strategy proposed in this paper can effectively improve the responsiveness of EV participation in the ancillary service market and increase the revenue of electric vehicle aggregator (EVA).

Optimal Storage Allocation for Delay Sensitivity Data in Electric Vehicle Network

For significant characteristics such as high efficiency and environmental protection, electric vehicles (EVs) have become a new technology trend in the intelligent transportation system (ITS). Data like real time traffic situation and charge point occupation in ITS shows significant real-time characteristics. Distributed storage system, an effective technology to ensure reliable sharing of dynamic data, is first adopted to storage allocation for delay-sensitive data in this article. In order to improve the recovery probability of delay-sensitive data within its timeliness, we establish an access queuing delay model based on the characteristics of sensitive data. Then, we find the optimal storage allocation strategy across distributed storage nodes for data with different delay threshold in terms of the recovery probability. The analysis shows that for data with low real-time performance and delay sensitivity, the maximal symmetric allocation is more excellent. For real-time data with low delay threshold, the minimal allocation is better under the condition of limited storage budget.

Data dissemination with interoperability in IoT network

SummaryInternet of Things (IoT) is connected to heterogeneous devices. Efficient adaptive scheduling with encoding and decoding of data is an unaddressed issue in IoT. This paper processes the data under three major hierarchy: namely, adaptability, scheduling of data, and network coding for that data. The reliable access to the information is ensured by a device which is a primary eminence in IoT. Device must be able to adapt itself according to the changes in the network and to maintain its reliability as well as transparency and seamless access to the resources. To enhance the performance of the data dissemination, the scheduling process is investigated using the spatial grouping in IoT devices; this is achieved by joint spatial and code domain scheduling scheme, and the novel preconfigured access scheme is coined in order to minimize the collision rate of arbitrary access; during the data dissemination, the erasure coding scheme is used for the encoding and decoding of packets which provides optimal redundancy. We carried the simulation using Contiki and it shows the proposed Polymorphic Erasure Coding with Markov decision Adaptability and Neural networks (PECMAN) improves in terms of cost, overhead, and delay when compared with Multi‐user Shared Access (EMUSA), Polynomial‐time Optimal Storage Allocation (OSA) scheme, and Event‐Aware Back pressure Scheduling Scheme (EABS).

Optimal Energy Storage Allocation for Mitigating the Unbalance in Active Distribution Network via Uncertainty Quantification

Voltage unbalance (VU) in an active distribution network (ADN) could result in increased network losses and even system instability. The additional uncertainties embedded in ADN might lead to serious VU problems with the proliferation of single-phase distributed energy resources (DERs). This paper proposes a two-stage uncertainty quantification and unbalance mitigation (UQUM) framework to cope with the corresponding VU problems and quantify and mitigate the impacts of variable DERs on VU. In Stage one, the global sensitivity analysis based on the Rosenblatt transformation (RT-based GSA) method is proposed to quantify the impacts of nonlinearly correlated DERs with tail dependence on VU. The RT-GSA method can identify critical DERs with significant impacts on VU. In Stage two, the joint allocations of fixed and mobile energy storage devices (ESDs) are considered in which an optimal mitigation strategy is proposed to alleviate VU and effectively compensate the critical DER fluctuations (identified in Stage one). Based on the given DER data and the available ESD capacity, the effectiveness of the proposed UQUM framework is verified in a 123-bus three-phase unbalanced ADN and the corresponding results are discussed.

Hybrid collaborative caching in mobile edge networks: An analytical approach

With caching at the base stations (BSs) in a cooperative manner, mobile edge caching (MEC) can alleviate the heavy backhaul burden and reduce the duplicated transmissions of content downloads, which has recently been considered a promising solution to cope with the exponentially increasing data traffic. However, how to maximize the storage utilization while reduce service latency and improve energy savings is still a critical issue in the large-scale mobile edge networks (MENs), since the growth of MENs in size as well as uneven users’ distribution make it difficult to determine which MEC should cache which content. To address this problem, we propose a hybrid collaborative caching (Hy-CoCa) design that jointly leverages local independent, intra-group collaborative and intra-network collaborative caching manners. MECs are clustered into disjoint groups, and then each MEC’s storage is partitioned into local, intra-group and intra-network portions. Local storage is reserved for storing the most popular contents to each MEC locally, so that users can directly fetch them from their associated MEC; Intra-group storage of different MECs inside the same group are regarded as an entity, which is used for collaboratively storing the middle popular contents, so as to reduce the probability for requesting contents from distant MECs; Intra-network storage of all MECs are leveraged for collaboratively storing less popular contents to different MECs in the entire MENs, as a means to improve the overall content diversity. Specifically, we first develop the Hy-CoCa’s framework to support users’ requests locally and conduct the construction of logical groups, considering users’ distribution and MECs’ proximity. Moreover, under the storage and popularity constraints, we formulate the storage allocation optimization problem to minimize average service latency and derive the optimal storage allocation. Furthermore, given an optimal storage allocation, we also formulate the request-aware content placement problem into an integer linear programming problem to maximize the overall energy savings. We prove the submodularity property of the objective function and propose a greedy algorithm with linear computational complexity, which can achieve (1−1/e)-optimality. Simulation results with real-world YouTube trace data demonstrate that our caching strategy can achieve 6% to 28% latency reduction and 9% to 75% energy savings improvement compared with other existing caching strategies.

Derivation of Hydropower Rules for Multireservoir Systems and Its Application for Optimal Reservoir Storage Allocation

AbstractHydropower operating rules can guide reservoir release and storage for maximizing hydropower generation. The optimal hydropower operating rules are usually derived by numerical hydropower s...

Cooperative Edge Caching in Software-Defined Hyper-Cellular Networks

In this paper, content caching is considered in a software-defined hyper-cellular network (SD-HCN) with capacity-limited backhaul connections. To achieve efficient content caching and delivery at the network edge, an analytical framework of minimizing the average content provisioning cost of SD-HCN, e.g., latency, bandwidth, and so on, is first formulated subjected to a sum storage capacity constraint. An optimal solution to this problem requires a joint design of storage allocation and content placement at the centralized control base station (CBS) and distributed traffic base stations (TBSs), which is NP-hard in general. To provide insights, a baseline non-cooperative caching strategy is first introduced between the CBS and TBSs. Then, an efficient cooperative edge caching strategy is proposed by leveraging the vertical cooperation between the CBS and TBSs, and horizontal cooperation between the TBSs. Analytical results demonstrate that the content provisioning cost of SD-HCN is significantly reduced by using the analytically obtained optimal storage allocation between the CBS and TBSs, and the proposed cooperative edge caching strategy always outperforms the non-cooperative caching strategy. Furthermore, by switching between the vertical and horizontal cooperative caching modes, extra performance gains can be achieved by the proposed cooperative edge caching strategy.

Operating strategy and optimal allocation of large‐scale VRB energy storage system in active distribution networks for solar/wind power applications

With the high penetration of renewable-type distributed generators (DGs), the maximum consumption of wind and solar energy has become an increasingly serious problem in active distribution networks. In this study, one simple and effective operating strategy for large-scale VRB ESS to maximise the use of VRB ESS was proposed considering the dynamic characteristics of VRB including efficiency and absorption performance. And then, a mathematical framework for optimal allocation of VRB ESS was proposed considering the maximum consumption of wind and solar energy, the total costs of VRB ESS and the total benefits of VRB ESS. This proposed mathematical framework was solved by a dynamic programming optimisation algorithm and has been applied to determine the optimal VRB power and energy ratings in a real distribution power system. The feasibility of the solution is ensured based on a per-day cost model and the simulation results clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method.

Impact of Optimal Storage Allocation on Price Volatility in Energy-Only Electricity Markets

Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms e.g. pump-storage hydro, large-scale or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping and price arbitrage capabilities. In this paper, we propose a stochastic bi-level optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage and transmission players is modeled at the lower level problem using an extended Cournot-based stochastic game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, is considered as the case study for the proposed storage allocation framework. Our numerical results indicate that 80% price volatility reduction in SA electricity market can be achieved by installing either 340 MWh regulated storage or 420 MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

Towards Green IoT Networking: Performance Optimization of Network Coding Based Communication and Reliable Storage

Internet of things (IoT) is expanding its outreach to almost every aspect of our daily life. By utilizing network coding in IoT, the IoT energy consumption can be reduced. Thus it is worthwhile studying and improving the applications in IoT, where network coding is incorporated. In this paper, we optimize the performance of network coding-based communication and reliable storage in two important components of IoT, including the IoT core network, where data is sensed and transmitted, and the distributed cloud storage, where the data generated by the IoT core network is stored. First, we propose an adaptive network coding scheme in the IoT core network to improve the transmission efficiency. We demonstrate the efficacy of the scheme and the performance advantage over existing schemes through simulations. Second, we introduce the optimal storage allocation problem in the network coding-based distributed cloud storage, which aims at searching for the most reliable allocation that distributes the $n$ data components into $N$ data centers, given the failure probability $p$ of each data center. Finally, we propose a polynomial-time optimal storage allocation (OSA) scheme to solve the problem. Both the theoretical analysis and the simulation results show that the storage reliability could be greatly improved by the OSA scheme.

Optimal Storage Allocation for Wireless Cloud Caching Systems With a Limited Sum Storage Capacity

In wireless cloud storage systems, the recovery failure probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For an efficient utilization of limited storage capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud storage system with a sum storage capacity constraint for both high SNR regime and low SNR regime. Then, we find the optimal storage allocation strategy across distributed storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor$ complete storage nodes and an incomplete storage node) is optimal for low SNR regime, where $T$ is the sum storage capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.

Optimal Coding and Allocation for Perfect Secrecy in Multiple Clouds

For a user to store data in the cloud, using services provided by multiple cloud storage providers (CSPs) is a promising approach to increase the level of data availability and confidentiality, as it is unlikely that different CSPs are out of service at the same time or collude with each other to extract information of a user. This paper investigates the problem of storing data reliably and securely in multiple CSPs constrained by given budgets with minimum cost. Previous works, with variations in problem formulations, typically tackle the problem by decoupling it into sub-problems and solve them separately. While such a decoupling approach is simple, the resultant solution is suboptimal. This paper is the first one which considers the problem as a whole and derives a jointly optimal coding and storage allocation scheme, which achieves perfect secrecy with minimum cost. The analytical result reveals that the optimal coding scheme is the nested maximum-distance-separable code and the optimal amount of data to be stored in the CSPs exhibits a certain structure. The exact parameters of the code and the exact storage amount to each CSP can be determined numerically by simple 2-D search.

Optimal storage allocation on throwboxes in Mobile Social Networks

In the context of Mobile Social Networks (MSNs), a type of wireless storage device called throwbox has emerged as a promising way to improve the efficiency of data delivery. Recent studies focus on the deployment of throwboxes to maximize data delivery opportunities. However, as a storage device, the storage usage of throwboxes has seldom been addressed by existing work. In this paper, the storage allocation of throwboxes is studied as two specific problems: (1) if throwboxes are fixed at particular places, how to allocate storage to the throwboxes; and (2) if throwboxes are deployable, how to conduct storage allocation in combination with throwbox deployment. Two optimization models are proposed to calculate the optimal storage allocation with a knowledge of the contact history of users. Real trace based simulations demonstrate that the proposed scheme is able to not only decrease data loss on throwboxes but also improve the efficiency of data delivery.