Energy Storage Investment Research Articles

The Economic Influence of Energy Storage Construction in the Context of New Power Systems

The increase in the proportion of renewable energy in a new power system requires supporting the construction of energy storage to provide support for a safe and stable power supply. In this paper, the computable general equilibrium (CGE) quantitative assessment model is used coupled with a carbon emission module to comprehensively analyze the benefits and costs of energy storage construction from a macro perspective. Taking Zhejiang Province as an example, the investment in and construction of energy storage under the new power system of the 14th Five-Year Plan will slow down the economic growth of Zhejiang Province to a slight extent, but this investment and construction can improve the income level of residents and further redistribute the income structure. At the same time, energy storage investment and construction are conducive to building a clean, low-carbon, and efficient power system. The construction of energy storage can smooth out changes in electricity demand, while enhancing the electricity consumption of the residential sector, making the core sector’s electricity consumption more efficient. In addition, the construction of energy storage in the new power system has a positive effect on carbon emission reduction in Zhejiang Province, with the CO2 growth rate being significantly lower than the baseline scenario.

Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

AbstractCloud energy storage is considered a promising application in future power systems. It focuses on optimally leveraging the capacity of centralized large‐scale energy storage compared with the requirements of small‐scale localized users. In this paper, to satisfy the small‐ and medium‐scale timely energy storage requirement from localized users, the concept of the cloud‐based location sharing energy storage is proposed. The modular mobile energy storage system is flexibly configured and deployed at different sites to fulfil the long‐term seasonally dynamic transformer capacity increment and short‐term daily energy arbitrage based on economic values. To optimize the overall incomes of the energy storage investment, a two‐step user potential identification algorithm is proposed to discover the most valuable users at different time scales from the regional power usage profile. Then, the number/capacity optimal planning algorithm is proposed to optimally share the mobile energy storage system among users seasonally and total profits are further increased through daily energy arbitrage at less valuable seasons. Finally, the structure and dispatching strategy of the cloud‐based location sharing energy storage management system is illustrated. Case study results prove that the proposed identification algorithm can excavate the most valuable users at different time scales and the optimal planning and operation strategy is able to guarantee the overall income benefits.

Underground Gravity Energy Storage: A Solution for Long-Term Energy Storage

Low-carbon energy transitions taking place worldwide are primarily driven by the integration of renewable energy sources such as wind and solar power. These variable renewable energy (VRE) sources require energy storage options to match energy demand reliably at different time scales. This article suggests using a gravitational-based energy storage method by making use of decommissioned underground mines as storage reservoirs, using a vertical shaft and electric motor/generators for lifting and dumping large volumes of sand. The proposed technology, called Underground Gravity Energy Storage (UGES), can discharge electricity by lowering large volumes of sand into an underground mine through the mine shaft. When there is excess electrical energy in the grid, UGES can store electricity by elevating sand from the mine and depositing it in upper storage sites on top of the mine. Unlike battery energy storage, the energy storage medium of UGES is sand, which means the self-discharge rate of the system is zero, enabling ultra-long energy storage times. Furthermore, the use of sand as storage media alleviates any risk for contaminating underground water resources as opposed to an underground pumped hydro storage alternative. UGES offers weekly to pluriannual energy storage cycles with energy storage investment costs of about 1 to 10 USD/kWh. The technology is estimated to have a global energy storage potential of 7 to 70 TWh and can support sustainable development, mainly by providing seasonal energy storage services.

A novel investment strategy for renewable-dominated power distribution networks

Aiming at the problem of insufficient adaptability to the new elements of the new power system in the current distribution network investment method, this paper innovatively proposes a distribution network investment method based on the new power system. By constructing a source-grid-load-storage-side investment calculation model, the investment in the new power system can be accurately calculated. First, the distributed power investment is calculated from the two aspects of new construction and renovation. Secondly, construct the grid investment demand and grid investment capacity measurement model, and obtain the grid side investment model by weighted summation. Then, a model for calculating the scale of investment that can be saved due to demand-side response is constructed, and the cost of demand response is subtracted to obtain a model for calculating the scale of investment that can be saved on the load side. Finally, the energy storage side investment calculation model is constructed from the power supply side, grid side, user-side energy storage investment, and energy storage investment benefit. The research results are applied to the empirical area, and scientific guidance is provided to realize the precise investment in the area.

Research on Investment Economic Evaluation of Flexible Regulating Resources such as Energy Storage in High Proportion New Energy Environment

With a high percentage of new energy scenarios, it has become a trend for flexible resources such as energy storage systems to participate in long-term planning. In this context, it is important to explore how energy storage systems are configured. Therefore, it is necessary to analyse the economics of energy storage and other resources to ensure that investments in energy storage and other resources are economically valuable under different future models. This paper firstly analyses the current situation of a high percentage of new energy in China, and secondly constructs a model for analysing the economics of flexible regulation resources such as energy storage. Under partial data based on the scenery storage project, the economic analysis under four models is derived, including cost and benefit analysis, investment benefit analysis, and break-even point analysis. Finally, the economic evaluation of investment in energy storage projects under different models is summarized based on the calculation results.It is concluded that different scenario models have different economic benefits, and the operation capacity of the four scenarios is higher than that of the existing models.

Cloud Energy Storage Investment by Collaboration of Microgrids for Profit and Reliability Enhancement Considering a TSO-DSO Yearly Reward

A shared pool of grid-scale storage resources called Cloud Energy Storage (CES) can bring substantial benefits to the economical and reliable operation of MGs. However, the investment cost of CES may not be affordable for a single microgrid (MG). As a solution, we propose an approach in which neighboring microgrids in a distribution network collaborate and form a multi-microgrid (multi-MG) to install a shared CES to increase their profit and improve their reliability. Different investment scenarios are evaluated by considering the yearly reward from TSO and DSO. For each of the investment scenarios, TSO and DSO give a yearly reward based on the contribution of CES in peak-shaving and distribution network operation yearly cost reduction. Afterward, a decision table is provided in which, for all investment scenarios, profit, reliability index based on expected energy not supplied (ENS), and TSO-DSO yearly reward are determined. Finally, the microgrids select one of the investment scenarios using a multi-attribute decision-making approach. Simulation results of a case study validate the effectiveness of the proposed collaborative decision-making framework in increasing the economic value of CES investment, reliability enhancement in multi-MG, and peak-shaving.

Energy storage economy research and sensitivity analysis applied to photovoltaic primary frequency modulation scenario

In practical engineering applications, the investment, income, subsidies and other costs of energy storage batteries have a certain impact on the overall economic benefits of primary frequency modulation. The larger the capacity of the configured battery energy storage system, the better the primary frequency modulation effect will be, but at the same time, the problem is that the cost of investment in the energy storage system will increase, which will offset the benefits of primary frequency modulation loss reduction of its auxiliary photovoltaic station from an economic perspective. Therefore, this paper will clarify the benefits and costs of the primary frequency modulation application environment of the energy storage system, and establish an economic analysis model from delaying the investment of photovoltaic station equipment, reducing the cost of light waste, environmental benefits, direct benefits, government subsidies, energy storage investment costs and other aspects. Finally, a photovoltaic station is taken as an example to simulate and analyse the sensitivity.

A Bi-level optimization model of integrated energy system considering wind power uncertainty

To cope with the volatility of renewable energy and improve the efficiency of energy storage investment, a bi-level (B-L) optimization model of an integrated energy system (IES) with multiple types of energy storage is established by considering the uncertainty of wind power. The upper-level optimization model considers the lowest configuration cost of energy storage as the objective function and satisfies the constraints of the energy storage configuration. The lower-level optimization model considers the lowest operation cost of the IES as the objective function and satisfies the constraints of the system operation. Second, to overcome the fluctuation problem of wind power output, a robust optimization theory is introduced to describe the uncertainty. Robust coefficients are set to reflect different risk attitudes, which improves the adaptability of the system to uncertainty. Third, the B-L optimization model is solved using the Karush–Kuhn Tucker condition. Finally, a new park is used to implement the simulation. The conclusions are as follows: (1) The economic configuration strategy and optimal operation scheme can be obtained by applying the B-L optimization model, and the upper- and lower-levels interact with each other. The optimal targets of the upper- and lower-level models are −115,848 ¥ and 57,131,102 ¥, respectively. (2) The robust optimization theory improves the ability of a system to deal with risks. Robust optimization theory improves the ability of a system to deal with risks. With an increase in the robustness coefficient, the profit space of the upper-level model increases; however, the operation cost of the lower-level model increases.

Energy Storage Investment and Operation in Efficient Electric Power Systems

We consider welfare-optimal investment in and operation of electric power systems with constant returns to scale in multiple available generation and storage technologies under perfect foresight. We extend a number of classic results on generation, derive conditions for investment and operations of storage technologies described by seven cost/performance parameters, and develop insights on power systems with multiple storage technologies. Simulation of a deeply decarbonized “Texas-like” power system with two available storage technologies shows both the non-existence of simple “merit-order” rules for storage operation and the value of frequency domain analysis to describe efficient operation. Our analysis points to the critical role of the capital cost of energy storage capacity in influencing efficient storage investment and operation.

Benefit analysis and preliminary decision-making of electrical and thermal energy storage in the regional integrated energy system

The ambitious goal of decarbonization has promoted regional integrated energy system (RIES), which enables interaction between energy consumption and supply sectors with different characteristics to improve energy application efficiency. Compared with single energy storage, multiple energy storage (MES) may have the potential to provide better flexibility and lower costs, but the necessity of applying and the preliminary decision-making principles have not been very clear to users and investors. To solve this problem, this study based on the typical RIES framework on the demand-side, firstly proposes multi-dimensional evaluation indexes from the aspects of renewable energy consumption, carbon emission reduction, load shifting and economic property; then, taking a commercial area as a case, develop the optimal operation strategy of the minimum total daily cost to analyze the operation and synergistic benefits under four scenarios; influencing factors such as energy storage capacity, load demand and region types are considered though sensitivity analysis; finally, suggestions for the preliminary decision-making of MES are given. The results show that the combination of electricity and thermal energy storage can realize the complementary advantages of single energy storage technology, making the contribution of different kinds of energy storage benefits more balanced on the basis of reducing the total cost, which significantly improves the flexibility of the energy system; the electricity-thermal energy storage has obvious advantages in the four functional areas involved herein; however, as the storage capacity increases to the saturation value, the marginal effect appears, and the corresponding total benefits tend to stabilize gradually. The preliminary decision-making of applying energy storage is carried out according to the external and internal levels, respectively according to the control requirements of energy storage investment cost, the allocation of renewable energy in the target area, as well as the regional energy consumption structure and specific needs. This principle can provide a reasonable basis for decision-makers and promote the application and development of multiple energy storage.

Strategic energy storage investments: A case study of the CAISO electricity market

Energy storage can provide a range of revenue streams for investors in electricity markets. However, as their deployments continue to rise, storage will no longer be a player on the sidelines and remain a price-taker, rather, these assets will start to impact prices. In this study, accounting for energy storage as a price-maker and using data from CAISO, we investigate strategic market behavior among competing investors using a non-cooperative game. We establish a centralized optimization problem to compute the market equilibrium. In the game, each investor initially decides on how much to invest and then how to operate their asset over a time horizon. Investors are allowed to deploy different energy storage technologies. Analytically, we show that an increasing number of investors will increase the market competition thereby reducing profits while increasing the total capacity of storage deployed. Numerically, our key findings include: (a) the difference in optimal investments under price-taker and price-maker assumptions, (b) as wind and solar assets expand under different resource-mix scenarios, there is a corresponding non-monotonic variation in arbitrage potential for energy storage, (c) when competition is intense and investors are allowed to invest in different technologies, a slight increase in storage technology performance can significantly improve an investor’s profit share, (d) when the arbitrage opportunity increases with renewables, longer duration storage technologies may gain an advantage, (e) Li-ion batteries dominate the market under low-moderate renewable energy penetration or with steep energy-capacity cost reductions in the future.

An Integrated Decision-Making Approach with Golden Cut and Bipolar q-ROFSs to Renewable Energy Storage Investments

An Integrated Decision-Making Approach with Golden Cut and Bipolar q-ROFSs to Renewable Energy Storage Investments

Energy storage capacity optimization of wind-energy storage hybrid power plant based on dynamic control strategy

The construction of wind-energy storage hybrid power plants is critical to improving the efficiency of wind energy utilization and reducing the burden of wind power uncertainty on the electric power system. However, the overall benefits of wind-energy storage system (WESS) must be improved further. In this study, a dynamic control strategy based on the state of charge (SOC) for WESS is proposed to maintain a healthy SOC for energy storage system (ESS). Then, four scenarios with different operation strategies are set based on the historical operation data of a wind farm in China. Particle swarm optimization is used to solve the problem, with the goal of optimizing the overall system benefit. The results show that the dynamic control strategy is the most cost-effective while maintaining reliability. When compared to other scenarios, the profit of the dynamic control strategy is extended by 7.63 %, 327.69 % and 9.75 % respectively, and the energy storage life is extended by 10.02 %, 62.89 % and 21.61 % respectively, demonstrating that different working state of energy storage will significantly affect the storage life and, ultimately, affect the economic benefit of the system. Finally, the influences of feed-in tariff, frequency regulation mileage price and energy storage investment cost on the optimal energy storage capacity and the overall benefit of wind-energy storage hybrid power plant are discussed.

Optimal configuration strategy of hybrid energy storage system on industrial load side based on frequency division algorithm

Electricity bills typically account for a large proportion of industrial users' production costs. Hybrid energy storage system (HESS), a high-performance energy storage method, has been widely used on the demand side. In the context of a two-part tariff system, the optimal configuration of battery-ultracapacitor HESS on the industrial load side realizes demand management and cost saving. Complete decoupling of load data promotes the full play to the advantages of batteries and ultracapacitors. Therefore, a VMD-ST-QF load frequency division strategy is proposed to decompose the chronological load curve into high-frequency and low-frequency components precisely. Depending on the results of frequency division, an optimal configuration strategy of HESS is established to minimize the net investment cost of energy storage. In this paper, the economic benefits of assembling energy storage on the industrial load side with four different configuration strategies are compared. The case study shows that the introduction of HESS based on the VMD-ST-QF strategy saves industrial users 5.26% of their total electricity cost and shortens the payback period of energy storage investment.

Maximizing self-consumption rates and power quality towards two-stage evaluation for solar energy and shared energy storage empowered microgrids

Substantial penetration levels of intermittent and fluctuated renewable energy resources like PV can cause overcapacity and other operational challenges in the grid. Therefore, energy market actors are directed to the feed-in limitations and restrict the installed capacity. Likewise, the declining incentives make energy storage central to increasing self-consumption. However, economic uncertainties arise concerns about the financial feasibility of energy storage investments. This study presents the techno-economic benefits in increasing PV self-consumption using shared energy storage for a prosumer community under various penetration rates. In the first stage, the optimal energy storage allocations were done using the proposed New Best Algorithm and genetic algorithm with Matlab. Then, the technical performance of the proposed method was simulated with year-long using PSS Sincal. In the second stage, the economic feasibility of increasing PV self-consumption using shared energy storage under various penetration rates is evaluated considering residual energy. The effects of incentives are examined in terms of economic indicators such as payback period, net present value, and internal rate of return. The incentives promote prosumers either with or without energy storage to increase self-consumption. As a result, shared energy storage increased self-consumption up to 11% within the prosumer community. Results and sensitivity analysis are given in detail. The proposed method provides significant economic benefits and improved power quality.

A Multi-Agent Game-Based Incremental Distribution Network Source–Load–Storage Collaborative Planning Method Considering Uncertainties

How to obtain the optimal decision-making scheme based on the investment behavior of various stakeholders is an important issue that needs to be solved urgently in incremental distribution network planning. To this end, this article introduces the virtual player “Nature” to realize the combination of the game theory and robust optimization and proposes an incremental distribution network source–load–storage collaborate planning method with a multi-agent game. First, the planning and decision-making models of a DG investment operator, a distribution network (DN) company, power consumers, and a distributed energy storage (DES) investment operator are constructed, respectively. Then the static game behaviors between the DG investment operator and distribution network company, as well as the DG investment operator and the DES investment operator, are analyzed based on the transfer relations between these four participants. At the same time, robust optimization is used to deal with the uncertainty of the DG output, and the virtual player “Nature” is introduced to study the dynamic game behavior between the DG investment operator and the distribution company. Finally, a dynamic–static joint game planning model is proposed. The simulation results verify the correctness and effectiveness of the proposed method.

Economic Analysis of Transactions in the Energy Storage Power Market: A Life-Cycle Cost Approach

Aiming at the impact of energy storage investment on production cost, market transaction and charge and discharge efficiency of energy storage, a research model of energy storage market transaction economic boundary taking into account the whole life cycle cost was proposed. Firstly, a peak-valley filling time division method based on equal capacity is proposed, which effectively improves the peak-valley time division and the accuracy of ES should scene switch. On this basis, a control strategy of “off-time reuse” is proposed to give full play to the function of “one standby multi-purpose” of energy storage, which improves the energy storage utilization rate and economic benefits. Secondly, an economic boundary model based on the life-cycle cost of energy storage and the evolution function of energy storage cost is constructed and solved by improved genetic algorithm. Finally, the simulation results show that compared with mono-peak control, the UTILIZATION rate of ES is increased by 16.25% and the investment recovery life is shortened by 1.17 years with “off-time reuse” strategy. Compared with the fixed division method, the investment recovery life of “peak clipping and valley filling” period division method is shortened by 1.75 years. In 2022, compared with the critical value of 0.76 yuan/kWh for fixed charge-discharge efficiency and cost, the critical value of the life-cycle cost model is 0.8 yuan/kWh, with an error of up to 5.26%.

Time-of-Use Pricing for Energy Storage Investment

Time-of-use (ToU) pricing is widely used by the electricity utility to shave peak load. Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals. However, without considering the implication on energy storage investment, an improperly designed ToU pricing scheme may lead to significant welfare loss, especially when users over-invest the storage, which leads to new energy consumption peaks. In this paper, we will study how to design a social-optimum ToU pricing scheme by explicitly considering its impact on storage investment. We model the interactions between the utility and users as a two-stage optimization problem. To resolve the challenge of asymmetric information due to users’ private storage cost, we propose a ToU pricing scheme based on different storage types and the aggregate demand per type. Each user does not need to reveal his private cost information. We can further compute the optimal ToU pricing with only a linear complexity. Simulations based on real-world data show that the suboptimality gap of our proposed ToU pricing, compared with the social optimum achieved under complete information, is less than 5%.

Investments in Energy Storage: Empirical Evidence from OECD Countries

Investments in Energy Storage: Empirical Evidence from OECD Countries

Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach

Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs; (2) peak shaving; (3) frequency regulation; and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments.