Storage Planning Research Articles

Consider the optimal allocation planning of energy storage for flexible resources of source network load

The increased penetration of distributed generation such as wind and light in the distribution grid causes dramatic fluctuations in net load, resulting in a sharp increase in the need for distribution grid flexibility. To this end, a distribution grid energy storage optimal allocation method considering source-net-load flexibility resources is proposed. First, a set of uncertain operation scenarios is established from multiple time scales using the K-means method and robust optimization theory; second, an economically flexible multi-objective optimization model with the minimum annual integrated cost and the highest average daily flexibility level of the distribution network as the optimization objectives is established and solved by a multi-objective composite differential evolutionary algorithm with embedded tide calculation. Arithmetic design and simulation are based on the IEEE 33-node distribution network system. The results validate the reasonableness and effectiveness of the proposed model considering source-grid-load flexibility resources in solving the energy storage allocation scheme.

Optimal Configuration of User-Side Energy Storage for Multi-Transformer Integrated Industrial Park Microgrid

Under a two-part tariff, the user-side installation of photovoltaic and energy storage systems can simultaneously lower the electricity charge and demand charge. How to plan the energy storage capacity and location against the backdrop of a fully installed photovoltaic system is a critical element in determining the economic benefits of users. In view of this, we propose an optimal configuration of user-side energy storage for a multi-transformer-integrated industrial park microgrid. First, the objective function of user-side energy storage planning is built with the income and cost of energy storage in the whole life cycle as the core elements. This is conducted by taking into consideration the time-of-use electricity price, demand price, on-grid electricity price, and energy storage operation and maintenance costs. Then, considering the load characteristics and bidirectional energy interaction of different nodes, a user-side decentralized energy storage configuration model is developed for a multi-transformer-integrated industrial park microgrid. Finally, combined with the engineering practice constraints, the configuration model is solved by mixed integer linear programming. The simulation test demonstrates how the proposed model can successfully increase the economic benefits of an industrial park. Electricity and demand costs are reduced by 11.90% and 19.35%, respectively, and the photovoltaic accommodation level is increased by 4.2%, compared to those without the installation of energy storage system.

A bi-level reinforcement learning model for optimal scheduling and planning of battery energy storage considering uncertainty in the energy-sharing community

Sharing of battery energy storage systems (BESS) in the energy community by reflecting the real world can play a significant role in achieving carbon neutrality. Therefore, this study aimed to develop a bi-level reinforcement learning (RL) model of BESS considering uncertainty in the energy-sharing community for the following optimization strategies: (i) short-term scheduling model for optimal electricity flows considering operational objectives (i.e., self-sufficiency rate (SSR), peak load, and economic profit); and (ii) long-term planning model for optimal BESS plan (i.e., install, replace, and disuse) along with battery types (new or reused batteries). A case study in the South Korea Nonhyeon neighborhood was conducted to evaluate the developed bi-level RL model feasibility based on future scenarios considering the time-dependent variables. The developed model increased economic profit by up to 18,830 USD compared to the rule-based model. Compared to the case where BESS was not installed, SSR increased by up to 7.79% and peak demand decreased by up to 1.31 kWh. These results show that the developed model could maximize the economic feasibility of community-shared BESS by reflecting the uncertainty in the real world, ultimately benefiting participants in the energy-sharing community.

System value and utilization performance analysis of grid-integrated energy storage technologies in Japan

Deploying utility-scale storage systems is expected to play a critical role in improving energy flexibility and economic performance considering rising variable renewable energy penetration. However, existing studies take the impacts of storage duration times and changes in the real-world electricity market into account less. This study develops a framework to evaluate and compare the opportunity value of different storage technologies as a price taker aiming at maximizing the arbitrage profit. Firstly, long-term operational data of real-world grids were collected, and results indicate that rising penetration of renewable energy generation increases the inter-annual volatility and decreases the average value of spot prices. Comparative results verified that the storage roundtrip efficiency, duration time and variation of spot prices impacted the storage arbitrage profits and utilization rates significantly. The rising volatility of spot prices is largely driven by the rapid expansion of renewable energy generation, adds more opportunity value to the storage system dispatch process, and amplifies the influence of storage duration. The results present the potentiality of deploying power to hydrogen as long-duration storage to deliver economic benefits by exploring the variations of the spot prices. Overall, a full understanding of how the role of storage systems changes provides insight into options for storage planning of renewable-dominated electricity systems.

A Two-Stage Stackelberg Game Wind-Storage Planning Model Considering a Bus Carbon Intensity Incentive Mechanism

In the trend of power system decarbonization, wind power and energy storage planning studies are in full swing. Incentives for wind power and energy storage development are important research topics. However, most studies have proposed incentives from an energy perspective, and few focus directly on incentives from a carbon perspective. To this end, this paper proposes a novel unified bus carbon intensity incentive mechanism from the carbon perspective. A two-stage Stackelberg game wind storage planning model that considers the proposed carbon incentive mechanism is constructed. In the first stage, the carbon flow of the entire system is obtained following economic dispatch based on the carbon emission flow theory. In the second stage, a Stackelberg game wind-storage planning model is constructed to model the game between the power generation company and the power grid company. Through simulation analyses of these cases, the proposed incentive mechanism can effectively guide the construction of wind turbines and energy storage at the best buses. Additionally, by comparing the proposed incentive with the existing incentive mechanisms, this paper finds that the proposed incentive mechanism can achieve a higher carbon reduction effect with a similar total investment in wind turbines and energy storage.

Economic Instruments and the Vision of Prosumer Energy in Poland. Analysis of the Potential Impacts of the “My Electricity” Program

Fossil fuels, which are widely used in Poland, have negative environmental and health consequences. Photovoltaics, used especially by households, contribute to a reduction in CO2 emissions and indirectly reduce the problem of smog. However, the high cost of photovoltaic installations forces the use of economic incentives, such as direct subsidies to the installation, under the “My Electricity” program (hereinafter referred to as the program). The development of photovoltaics is hindered by the ineffective functioning of transmission grids. In the current edition of the “My Electricity” program, the main emphasis was placed on the use of electricity at the place of generation through the use of energy storage facilities and the transition to a new billing system, so-called net billing. The program indirectly contributes to the growing popularity of heat pumps as an installation that uses electricity generated on-site. Significant changes in the program inspired the authors of the study to plan and conduct nationwide research; they assessed investment plans for energy storage and heat pumps by potential individual investors, i.e., owners of single-family houses. Based on the results obtained, the authors of the article concluded that the new version of the “My Electricity” program will almost exclusively affect the energy storage market. This impact will stimulate the development and application of this technology. In the case of the heat pump market, the current demand will remain at the same level. The program will contribute to achieving the assumed goals, especially energy consumption at the place of production and reducing so-called low emissions. The program will not affect the demand for heat pumps.

Physico-chemical assessment of on-farm bioconversion of organic waste in dairy farms in context to sustainability and circular bioeconomy

ABSTRACT On a milk-producing dairy farm, milk production is correlated with manure production and the number of cattle, and manure is widely used as a soil fertilizer. However, excessive dairy manure production is linked with greenhouse gas emissions and water quality issues. On-farm planning of manure storage and application to enhance soil nutrients are essential in a circular economy to reduce environmental impact, where manure is not landfilled and incinerated. Instead, it creates a nutrient resource for crops and soil. Dairy manure, which is rich in nutrients, is a valuable fertilizer that contains many nutrients such as nitrogen (N), organic matter (OM), phosphorous (P), Potassium (K) and micronutrients. In this work, a pilot field research was conducted between 2016 and 2018 in various parts of California, USA (San Joaquin Valley, Sacramento Valley, Shasta Cascade, and the North Coast of California) to assess physio-chemical characteristics of solid fractions of dairy manure among various dairy farms. A total of 156 samples were collected from the gut (n = 107) and toe (n = 49) of the manure piles across California for determining total solid (TS), volatile solid (VS), temperature, moisture content and carbon-nitrogen ratio (C: N). Here, using the observations of field study and analysis, we show that C: N, OM and MC of solid fractions of dairy manure vary significantly among dairy farms. The average C: N ratio of manure (26–32) among various regions was close to an ideal C: N value of 24:1 for soil microbes to stimulate nutrient release to crops. Manure pH ranged between 7.0 and 8.0, which was close to an optimal pH range for common crops (6.0–8.0). Moreover, considering less cost and surplus availability, manure will likely continue providing a cost-effective organic fertilizer resource compared to commercial chemical fertilizers.

Optimal configuration of grid-side energy storage considering static security of power system

The large-scale access of distributed sources to the grid has brought great challenges to the safe and stable operation of the grid. At the same time, energy storage equipment is of great importance to effectively enhance the consumption of renewable energy and ensure the safe and stable operation of the grid. This paper proposes a method for optimal allocation of grid-side energy storage considering static security, which is based on stochastic power flow analysis under semi-invariant method. Firstly,according to the load, wind power and photovoltaic probability model, a system stochastic power flow model is constructed. Furthermore, the fault probability and fault severity indicators are established from two dimensions of branch power flow and node voltage. And combine the fault probability and severity indicators to establish a static security assessment indicators system. Then, a grid-side energy storage planning model is constructed from the perspective of energy storage operators. Finally, an improved genetic algorithm is used to solve the two-stage planning and operation problem proposed in this paper, and simulation analysis is conducted based on the IEEE-30 node system. The results show that the energy storage configuration considering static security constraints can effectively reduce the fault probability and the severity of fault overlimit. The simulation and case study verify that the proposed energy storage allocation method can effectively improve the static security of the system.

Emissions and prices are anticorrelated in Australia’s electricity grid, undermining the potential of energy storage to support decarbonisation

Recent work has shown that energy storage operating in a CO2 intensive grid can increase greenhouse gas (GHG) emissions. In this paper we sought to characterise the emissions of Australia’s electricity grid to inform the planning and operation of energy storage with the goal of minimising emissions associated with energy storage and supporting the rapid decarbonisation of Australia’s energy system.To do so, a marginal emissions factor (MEF), representative of the emissions intensity of the marginal generator (the generator with the highest bid price) in each time period, was calculated for the Australian National Electricity Market (NEM) from 2013 to 2021. Through analysis, significant variation was discovered in the MEF’s intra-day variability, with high MEF values occurring overnight and low MEF values occurring during times of peak demand. Compared against the average 30-min spot price across the day, a strong anti-correlation was calculated between the MEF and the spot price.Using these results, the importance of energy storage operated to minimise both costs and emissions was highlighted. By taking the key finding of its anti-correlation with price, the MEF can be simply implemented in the real world, including through dynamic carbon incentives and market tariffs, to ensure emissions are being reduced both in the short and long-term.

Ten simple rules for using public biological data for your research.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

Optimization planning of energy storage based on multi-objective improved particle swarm and droop control algorithm

Optimization planning of energy storage based on multi-objective improved particle swarm and droop control algorithm

Retracted: Path Planning of Storage and Logistics Mobile Robot Based on ACA‐E Algorithm

Retracted: Path Planning of Storage and Logistics Mobile Robot Based on ACA‐E Algorithm

Double-layer optimized configuration of distributed energy storage and transformer capacity in distribution network

In order to solve the problem of low utilization of distribution network equipment and distributed generation (DG) caused by expansion and transformation of traditional transformer capacity, considering the relatively high cost of energy storage at this stage, a coordinated capacity configuration planning method for transformer expansion and distributed energy storage (DES) is proposed in this paper. First, the energy storage capacity requirements is analyzed on the basis of the transformer overload requirements, and analyzing the correspondence between different capacities of energy storage and transformer expansion capacities. Besides, taking into account the impact of different action mechanisms of energy storage on the node load within a day, a location optimization method for DES based on the standard deviation of network loss sensitivity is introduced. Then, considering the net cost of coordinated planning of energy storage and transformer are minimum and the benefit of energy storage operation is maximum, a two-layer optimization model of distributed energy storage and transformer capacity is established. Finally, the solution method of the two-layer optimization model is proposed, and the feasibility of the proposed model is verified by IEEE33 bus distribution system. The results show that the coordinated planning method proposed in this paper can greatly reduce the investment cost, and the net cost of the coordinated planning scheme is reduced by 17.558 million yuan compared with the scheme of separate configuration for energy storage, which effectively improves the economics of energy storage configuration.

Optimal capacity planning and operation of shared energy storage system for large-scale photovoltaic integrated 5G base stations

Shared energy storage (SES) system can provide energy storage capacity leasing services for large-scale PV integrated 5G base stations (BSs), reducing the energy cost of 5G BS and achieving high efficiency utilization of energy storage capacity resources. However, the capacity planning and operation optimization of SES system involves the coordinated operation and cost sharing between SES system and large-scale 5G BSs, which is highly coupled and greatly increases the complexity of joint optimization. In this paper, a joint optimization method of SES system capacity planning and operation for large-scale PV integrated 5G BSs with energy storage planning requirements is proposed. First, a dynamic capacity leasing model of SES system is proposed with consideration of the power supply and load demand characteristics of large-scale 5G BSs. Second, a bi-level joint optimization problem is formulated to minimize the capacity planning and operation cost of SES system and the operation cost of large-scale 5G BSs based on the bi-level mixed-integer programming (BiMIP) model. Then, a reformulation and decomposition (R&D)-based SES system capacity planning and operation joint optimization (SESSION) algorithm for large-scale PV integrated 5G BSs is proposed, which is employed to reformulate the proposed bi-level joint optimization problem into a master problem and two subproblems. The independent solutions of the upper-level problem and the lower-level problem are achieved through the iteration between master problem and subproblems. Finally, the effectiveness and scalability of the proposed algorithm is verified through case studies, which demonstrate that the capacity planning and operation optimization of SES system can effectively achieve mutual benefits between large-scale PV integrated 5G BSs and SES system.

Optimal planning of lithium ion battery energy storage for microgrid applications: Considering capacity degradation

By adding battery energy storage (BES) to a microgrid and proper battery charge and discharge management, the microgrid operating costs can be significantly reduced. But energy storage costs are added to the microgrid costs, and energy storage size must be determined in a way that minimizes the total operating costs and energy storage costs. This paper presents a new method for determining the optimal size of the battery energy storage by considering the process of battery capacity degradation. In this method, initially, the battery degradation functions are modeled; then, these functions are incorporated in the problem of determining the optimal battery size. The impact of different cycles with various depths of discharge is also considered that is the prominence of the present work. The proposed method is formulated as mixed-integer linear programming (MILP). Notably, the degradation model was implemented through two different formulations, by considering cycles with various depths of discharge in the second formulation, and the results of each formulation is compared that demonstrates the effectiveness of the second formulation.

EBSF: Node Characteristics-Based Block Allocation Plans for Efficient Blockchain Storage

The heavy storage problem has become a key obstacle to the application of blockchain to the actual business environments, because each node needs to keep a complete replica of blockchain data. The data volume grows undesirably large in practice. It prevents the widely used devices, e.g., tablets and mobile phones, to join blockchain systems due to their limited storage and computing resource. Previous work addressed the storage issue by allowing participating nodes to only keep a fraction of the entire transaction set, e.g., sharding. However, existing studies focus on transaction placement with the minimum cross-shard communications. These studies neglect the node characteristics (e.g., storage capacity, cost, and response capability), which impacts the storage performance adversely. In this paper, we propose EBSF, a block storage framework that achieves efficient block storage by constructing a block allocation plan based on node characteristics. Blockchain nodes are organized into committees such that nodes in a committee work together to maintain the entire blockchain data. We formulate the block allocation plan problem that assigns each block to at least one node in a committee. The goal is to minimize the total cost while reaching the threshold of the response capability of each block. We prove the NP-hardness of the problem and propose heuristic algorithms. We also propose two strategies to handle the dynamic scenario of new blocks. Extensive evaluation shows the efficiency and effectiveness of the proposed framework.

A selection scheme for energy storage to participate in primary frequency regulation of new energy

The choice of energy storage is the key content of the energy storage plan and design to participate in the primary frequency regulation of new energy. It involves many indicators such as safety, primary frequency modulation adaptability, economy, and environmental performance, and is a complex multi-objective decision-making problem. This paper analyses the characteristics of each index and constructs a two-level decision-making system. According to expert scores and practical application requirements, the weight of each decision-making index is obtained by the AHP method. The TOPSIS method is further used to select energy storage according to the weight of each index and related parameters.

Low-Carbon Transition Pathway Planning of Regional Power Systems with Electricity-Hydrogen Synergy

Hydrogen energy leads us in an important direction in the development of clean energy, and the comprehensive utilization of hydrogen energy is crucial for the low-carbon transformation of the power sector. In this paper, the demand for hydrogen energy in various fields is predicted based on the support vector regression algorithm, which can be converted into an equivalent electrical load when it is all produced from water electrolysis. Then, the investment costs of power generators and hydrogen energy equipment are forecast considering uncertainty. Furthermore, a planning model is established with the forecast data, initial installed capacity and targets for carbon emission reduction as inputs, and the installed capacity as well as share of various power supply and annual carbon emissions as outputs. Taking Gansu Province of China as an example, the changes of power supply structure and carbon emissions under different scenarios are analysed. It can be found that hydrogen production through water electrolysis powered by renewable energy can reduce carbon emissions but will increase the demand for renewable energy generators. Appropriate planning of hydrogen storage can reduce the overall investment cost and promote a low carbon transition of the power system.

Comprehensive configuration strategy of energy storage allocation and line upgrading for distribution networks considering a high proportion of integrated photovoltaics

AbstractThe rapid development of photovoltaics (PVs) and load caused a significant increase in peak loads and peak‐valley differences in rural distribution networks, which require load peak shifting and line upgrading. Large peak‐valley differences also bring challenges on the safe operation of the utility power grid. Considering the integration of a high proportion of PVs, this study establishes a bilevel comprehensive configuration model for energy storage allocation and line upgrading in distribution networks, which can reduce peak loads and peak‐valley differences. In the upper level, a minimum annual planning cost is obtained by developing the installation capacity of centralised energy storage in transformer stations, the installation location and capacity of decentralised energy storage on lines and a scheme of line upgrading. In the lower level, the minimum total annual operation cost of the distribution network is obtained by developing an optimal scheduling for the centralised energy storage in transformer stations and decentralised energy storage on lines. Simulation results show that compared with the conventional energy storage planning strategy, the configuration investment can be reduced by 467.66 million yuan at least with the proposed strategy.

Multi-stage expansion planning of energy storage integrated soft open points considering tie-line reconstruction

With the rapid development of flexible interconnection technology in active distribution networks (ADNs), many power electronic devices have been employed to improve system operational performance. As a novel fully-controlled power electronic device, energy storage integrated soft open point (ESOP) is gradually replacing traditional switches. This can significantly enhance the controllability of ADNs. To facilitate the utilization of ESOP, device locations and capacities should be configured optimally. Thus, this paper proposes a multi-stage expansion planning method of ESOP with the consideration of tie-line reconstruction. First, based on multi-terminal modular design characteristics, the ESOP planning model is established. A multi-stage planning framework of ESOP is then presented, in which the evolutionary relationship among different planning schemes is analyzed. Based on this framework, a multi-stage planning method of ESOP with consideration of tie-line reconstruction is subsequently proposed. Finally, case studies are conducted on a modified practical distribution network, and the cost–benefit analysis of device and multiple impact factors are given to prove the effectiveness of the proposed method.