Energy Storage Investment Research Articles

Fully Parallel Algorithm for Energy Storage Capacity Planning Under Joint Capacity and Energy Markets

Energy storage (ES), with its flexible characteristics, has been gaining attention in recent years. The ES planning problem is highly significant to establishing better utilization of ES in power systems, but different market regulations impact the ES planning strategy. Thus, this paper proposes a novel ES capacity planning model under the joint capacity and energy markets, which aims to minimize the total cost for power consumers. The great challenge is that the ES planning model has a large number of time periods, which significantly increases the problem dimensionality. In order to alleviate the computational burden, a fully parallel algorithm is proposed to temporally decompose the original problem into a series of small sub-problems, which can be solved in parallel. Moreover, we find that the corresponding analytical solutions to the sub-problems remarkably accelerate the calculation speed while ensuring accurate results. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Energy storage (ES) has become more and more essential to guaranteeing power balance in power and energy systems by shifting peak loads to valley loads. However, investors may face challenges to ES capacity planning due to the lack of business models. To address this challenge, price tariffs should be carefully investigated. In the practical power system, the market price should consider both the energy price and capacity price for industries and big companies. In the energy market, investors can gain a profit by selling energy at the peak load (high price) and buying energy in the valley (low price). It should be noted that the energy market cannot recover the ES investment cost, but investors can, in fact, reduce their capacity cost since ES can reduce the peak load. Consequently, we have designed a new business model for ES planning under joint capacity and energy markets to analyze the profits via the two market regulations. The computational burden is another challenge for the proposed multi-period convex optimization model. The model must consider a long-term simulation, potentially containing thousands of time periods, which can be difficult to solve. In order to alleviate the computational burden resulting from the long-term market simulation, we further propose a fully parallel algorithm to solve the proposed business model for ES quickly. To sum up, the proposed model and method have been tested on a practical company with a practical price tariff in China to show their effectiveness.

Energy Storage Dynamic Configuration of Active Distribution Networks—Joint Planning of Grid Structures

The integration of distributed power generation mainly consisting of photovoltaic and wind power into active distribution networks can lead to safety accidents in grid operation. At the same time, climate change can also cause voltage fluctuations, direct current injection, harmonic pollution, frequency fluctuations, and other issues. To achieve economic and safe operation of the distribution network, an active distribution network-network planning model considering the dynamic configuration of energy storage system energy storage is constructed. This model focuses on energy storage batteries with high ease of use, high modularity, and strong mobility. The route location planning involving different load operating scenarios in spring, summer, autumn, and winter is constructed. The objective function includes the revenue from selling electricity in the distribution network, the expenditure on purchasing electricity in the distribution network, and the cost during the planned construction period. The constraints include three major constraints: distribution network operation, network topology, and energy storage system operation. Three numerical examples are set up to analyze the impact of energy storage system dynamic configuration on grid planning. The results confirmed the active distribution network-grid planning model for dynamic configuration of energy storage systems. Both Example 2 and Example 3 had 3 ESS configurations. Case 3 showed different access methods for ESS in different seasons. The access nodes for ESS in spring and winter were 4, 5, and 6, while the access nodes for summer and autumn were 3, 5, and 6. After applying the proposed method, the daily energy storage investment of commercial, residential, and industrial users gradually stabilized. The reliability of electricity consumption was improved, with an improvement rate of about 40%. The research has brought considerable economic benefits to distribution network operators. It has forward-looking academic value in the joint planning of grid structures and energy storage.

Collaborative Optimal Configuration of a Mobile Energy Storage System and a Stationary Energy Storage System to Cope with Regional Grid Blackouts in Extreme Scenarios

To address regional blackouts in distribution networks caused by extreme accidents, a collaborative optimization configuration method with both a Mobile Energy Storage System (MESS) and a Stationary Energy Storage System (SESS), which can provide emergency power support in areas of power loss, is proposed. First, a time–space model of MESS with a coupled transportation network and power grids is constructed, as a MESS is more flexible than a SESS. Considering resilience and recovery, a minimization objective function for total cost, encompassing the hybrid energy storage investment cost, the power grid operation cost, and the load shedding penalty cost, is established. Moreover, considering SESS constraints and operational constraints, a hybrid configuration model is established. Then, considering the probability of extreme accidents, the scenario analysis method is used to address randomness, ensuring that the configuration results can be adapted to various scenarios. The proposed method can fully combine the time–space flexibility of MESS and the economic advantages of SESS, which can reduce the total cost and ensure the power system’s reliability. Finally, the effectiveness of the proposed method is verified by the improved IEEE33 system.

Optimal configuration of multi microgrid electric hydrogen hybrid energy storage capacity based on distributed robustness

The combination of energy storage and microgrids is an important technical path to address the uncertainty of distributed wind and solar resources and reduce their impact on the safety and stability of large power grids. With the increasing penetration rate of distributed wind and solar power generation, how to optimize capacity configuration of hybrid energy storage capacity to improve system economy and reliability has become a research hotspot. This article establishes a multi microgrid interaction system with electric‑hydrogen hybrid energy storage. The microgrid system uses distributed wind and solar power as the power source. Then, considering the uncertainty of wind and solar power, a distributed robust model with the goal of system operation economy and reliability was established. This model is used to optimize the configuration of energy storage capacity for electric‑hydrogen hybrid energy storage multi microgrid system and compare the economic costs of the system under different energy storage plans. Finally, the article analyzes the impact of key factors such as hydrogen energy storage investment cost, hydrogen price, and system loss rate on energy storage capacity. The results indicate that reducing the investment cost of hydrogen energy storage is the key to reduce operating cost of multi microgrid hybrid energy storage system.

Optimal sizing and techno-economic analysis of the hybrid PV-battery-cooling storage system for commercial buildings in China

Energy systems for flexibility in buildings are hybrid, primarily including rooftop photovoltaics (PV), cooling storage, and battery. Considering their techno-economic patterns, this research establishes an optimization model to determine the optimal technology portfolio and financial advantages of PV-battery-cooling storage systems for commercial buildings in China. The analysis of all cases indicates that cooling storage outperforms batteries in economic benefits, suggesting the prioritization of cooling storage installation. Once the optimal cooling storage rate is exceeded, it is advisable to proceed with batteries. Meanwhile, PV integration significantly enhances the system efficiency and promotes battery utilization. For example, a 40% PV penetration combined with a 0.006 $/(a·kWhe) energy storage investment results in an impressive 27.3% cost reduction in a Beijing mall, while the optimal cooling storage rate decreases from 55% to 40%. Furthermore, the study emphasizes the impact of tariff patterns and electricity demand on the economic feasibility of hybrid energy systems. The museum's substantial annual cooling requirements and nighttime loads make cooling storage favorable, with PV less suitable than the mall and office. Notably, cities like Beijing, Shanghai, Chongqing, and Guangzhou exhibit considerable peak-to-valley tariff differences, yielding higher economic benefits ranging from 23% to 27%. Finally, as battery costs decline and electricity price becomes more volatile, the battery would gradually replace cooling storage, especially when battery cost drops from 150 $/kWh to 70 $/kWh.

Electric Vehicle Charging Station Power Supply Optimization with V2X Capabilities Based on Mixed-Integer Linear Programming

The European Union is committed to both lowering greenhouse gas emissions and promoting the adoption of electric vehicles (EVs) on its roads. To achieve these goals, it is imperative to speed up the development of the charging infrastructure as well as to ensure the effective integration of the charging infrastructure into distribution networks. Given that EV charging costs significantly contribute to the total cost of owning an EV, it is important to hedge against rising electricity prices and ensure affordable charging for the end users. Connecting solar power plants and battery storage to the electric vehicle charging stations (EVCSs) serves as a measure of hedging against potential future electricity price increases but also as an option that can contribute to reducing impact on the distribution network loading. In addition to this, connecting EVCS through grid connections of existing consumers (office/residential buildings, shopping malls, etc.) can reduce grid connection costs for EVCS but also contribute to electricity cost reduction for both EVCS and existing end consumers. Additionally, by integrating advanced charging strategies like the vehicle-to-everything (V2X) approach, the overall charging costs can be reduced even further. This paper focuses on optimizing the power supply and operation of EVCS by considering strategic investments in grid connection, photovoltaic plants, and battery energy storage. The research explores the potential savings derived from reduced energy/charging costs, along with the reduction in peak power expenses for different power supply options. In addition to this, the research explores the effect of different EV charging strategies as well as EVCS grid connection on optimal investments and total system costs. The combined investment and energy management problem is focused on determining the optimal EVCS power supply and operation while minimizing total investment and operation expenditures over the project lifetime. The underlying optimization problems for different supply scenarios are cast as mixed-integer linear programming problems that can be solved efficiently. The results show the influence of different grid connection options and EV charging strategies on the joint operation and costs of EVCS and existing buildings.

Evidence of households' demand flexibility in response to variable hourly electricity prices – Results from a comprehensive field experiment in Norway

Demand-side flexibility which reacts to hourly price signals is expected to play an essential role in balancing intermittent electricity generation in the future carbon-neutral power system. This study investigates the price responsiveness of households and various influencing factors through a pricing experiment which was implemented as a reward scheme and involved 3746 Norwegian households. These households, characterised by a highly electrified energy usage such as electric heating and charging electric cars, were subjected to variable hourly price signals with a one-day advance notification over the course of three winter months. The study reveals that households reduced their electricity demand by, on average, 2.92% in hours with high prices, and the reduction did not diminish significantly after repeated interventions. Moreover, an increased response could be observed for price signals with a short peak price period and when prices exceeded a threshold of 15 NOK/kWh. These results suggest that despite the limited potential of manual demand response from households, it can still be relied upon and utilised to enhance power system operations and planning. Additionally, to fully exploit its potential, it is recommended to enhance price responsiveness through tailored price information and by incentivising investments in automatic response and energy storage.

A novel fuzzy decision-making methodology for ranking energy storage investments in emerging economies

This study aims to evaluate essential performance indicators of energy storage investments with a novel decision-making model. The first stage includes weighting the selected criteria with Spherical fuzzy TOPSIS-based DEMATEL, called TOP-DEMATEL. In the second stage, seven emerging countries are ranked based on the effectiveness of energy storage investments using ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS). RATGOS is a new ranking method proposed in this study to address the shortcomings of the existing methods by using geometric mean. The findings indicate that energy storage capacity is the most significant factor in improving energy storage investments in developing economies. Technological improvements are also important in this regard. It is strongly recommended that energy storage technologies need to be developed by conducting new research and development activities. Owing to new technological developments, more efficient batteries can be produced. These new products allow energy to be stored at higher capacity.

Sensitivity analysis of levelized cost of hydro-pneumatic electricity storage for grid support applications

The first sensitivity analysis of hydro-pneumatic levelized cost of electricity storage for a set of twelve power system applications, ranging from primary response to seasonal storage, is done, using as parameters the specific power and energy investment costs and round-trip efficiency. The electric-to-electric round-trip efficiency for hydro-pneumatic energy storage systems is discussed, using a lab-scale hydro-pneumatic energy storage system that was built using commercially available bladder hydraulic accumulators. In general, the results show that hydro-pneumatic energy storage can be very competitive whenever the number of annual cycles is sufficiently high, as happens in the case of primary response application but, even for applications with a low number of annual cycles, it can be competitive for applications with a high ratio power/capacity. A comparison with the dominant technologies, pumped hydro and Li-Ion, shows that, for the highest hydro-pneumatic energy storage investment costs considered, this technology will only be competitive for primary response, but, for the lowest investment costs considered, hydro-pneumatic energy storage is more cost-effective than Li-Ion for all applications, and can even be more cost-effective than pumped hydro in four of the twelve applications. The main challenges to the development of hydro-pneumatic energy storage systems are discussed.

When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments

Globally, 1.5 billion people live off the grid, with their only access to electricity often limited to operationally expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and nonexistent at night. Thanks to recent technological advances, which have made large-scale electricity storage economically viable, a combination of solar generation and storage holds the promise of cheaper, greener, and more reliable off-grid power in the future. Still, it is not yet well understood how to jointly determine optimal capacity levels for renewable generation and storage. Our work aims to shed light on this question by developing a model of strategic capacity investment in both renewable generation and storage to match demand with supply in off-grid use cases while relying on fossil fuel as backup. Despite the complexity of the underlying model, we are able to extract two general results. First, we find that solar capacity and storage capacity are strategic complements, except in cases with very high investment in generation capacity, when they surprisingly turn into strategic substitutes with implications for long-term investment decisions. Second, we develop a simple heuristic to determine which storage technology, within a given portfolio, can turn a profit in the broadest set of market conditions and thus, is likely to be adopted first. We find that currently, low-efficiency, cheap technologies, such as thermal, can more easily turn a profit in off-grid applications than high-efficiency, expensive ones, such as lithium-ion batteries. We then develop two newsvendor-like approximations of the general model that are analytically tractable, yield precise values for the optimal investment decisions and profit in some cases, and provide bounds to the optimal investment decisions and profits in all other cases. To conclude, we calibrate our models to measure the accuracy of our solutions utilizing real-life data from three geographically-diverse islands, and then, we use our approximations to provide high-level insights on the role that large-scale storage will play in the years ahead as technology improves, carbon taxes are levied, and solar becomes cheaper. This paper was accepted by Beril Toktay, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the Mack Institute for Innovation Management at the Wharton School as well as the Kleinman Center for Energy Policy at the University of Pennsylvania. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2021.04129 .

An Optimal Configuration Model for Supercapacitor Capacity to Suppress Wind Power Fluctuations

This paper presents an optimization configuration scheme for energy storage capacity by taking into account the operational characteristics of supercapacitors. The scheme utilizes Empirical Mode Decomposition (EMD) to address power fluctuations during the integration of wind power into the grid. To begin with, the wind power data undergoes EMD to decompose it into different-order intrinsic mode functions. These functions are then reconstructed as low-frequency components and high-frequency components. The low-frequency components are directly connected to the grid, while the high-frequency components are employed for power smoothing through energy storage. Subsequently, the impact of various wind power grid limits on the directly connected power is compared to identify the optimal value. A model is established to configure the capacity of supercapacitors, aiming to mitigate wind power fluctuations. The model considers an objective function that minimizes the sum of energy storage investment and operational maintenance costs, along with the compensation cost associated with wind power fluctuation opportunities. Finally, the proposed model’s effectiveness and economic feasibility are validated through case studies. This ensures that the model’s efficacy in reducing power fluctuations and its viability from a financial standpoint are thoroughly examined.

Optimal Configuration of Wind-PV and Energy Storage in Large Clean Energy Bases

The installed capacity of energy storage in China has increased dramatically due to the national power system reform and the integration of large scale renewable energy with other sources. To support the construction of large-scale energy bases and optimizes the performance of thermal power plants, the research on the corporation mode between energy storage and thermal energy, including the optimization of energy-storage capacity and its operation in large-scale clean energy bases. In this paper, a large-scale clean energy base system is modeled with EBSILON and a capacity calculation method is established by minimizing the investment cost and energy storage capacity of the power system and constraints such as power balance, SOC, and power fluctuations. The research proposed a method of using coupled system of thermal energy storage systems primarily based on molten salt thermal storage and thermal power generation for rough modulation and using battery energy storage system for fine modulation tasks. Example of fine modulation includes frequency modulation and heating demand of the district, which significantly reduces the energy storage investment by more than 95%. A case study of a 10 MW clean energy base is conducted. The result shows that the overall pre-tax internal rate of return of the base project is 8%, which has good economic benefits.

On the Distributed Energy Storage Investment and Operations

Problem definition: Energy storage has become an indispensable part of power distribution systems, necessitating prudent investment decisions. We analyze an energy storage facility location problem and compare the benefits of centralized storage (adjacent to a central energy generation site) versus distributed storage (localized at demand sites). This problem encompasses optimizing storage capacities across all locations, with the objective of minimizing the total storage investment and energy generation costs. Methodology/results: We employ a stylized model that captures essential features of an energy distribution system, including convex costs, stochastic demand, storage efficiency, and line losses. Using dynamic programming, we optimize storage operations and derive value function properties that are key to analyzing the storage investment decisions. We discern fundamental differences between centralization/localization decisions at the capacity investment stage and the centralization/localization decisions at the storage operations level. Operationally, centrally stored energy offers more flexibility, which is consistent with the conventional understanding of inventory pooling. However, we find that localized storage often emerges as the preferred option at the investment stage under various circumstances. Managerial implications: Storage investment should first be made at the demand locations with positive minimum demand regardless of the level of demand variability. Subsequent storage investment should consider the tradeoffs between centralized versus localized investment. Operationally, the relative magnitudes of storage and line losses drive different optimal storage policies. Despite the differences, these policies are guided by common principles such as pooling inventory and balancing local storage levels. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2020.0652 .

Merchant Energy Storage Investment Analysis Considering Multi-Energy Integration

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology is a key means through which to deal with the instability of modern energy sources. One of the key development paths in the electricity market is the development by energy merchants of energy storage power plants in the distribution network to engage in a grid demand response. This research proposes a two-stage energy storage configuration approach for a cold-heat-power multi-energy complementary multi-microgrid system. Considering the future bulk connections of distributed power generation, the two most critical points of energy storage station construction are the power generation equipment and specific scenarios for serving the community, as well as the purchase and sale price of electricity for serving the community microgrid (which directly affects the investment revenue). Therefore, this paper focuses on analyzing the different impacts caused by these two issues; namely, the two most important concerns for the construction of energy storage configurations. First, the basic model enabling wholesale electricity traders to construct energy storage power plants is presented. Second, for a multi-microgrid system with a complementary cold-heat-power multi-energy scenario, a two-stage optimum allocation model is constructed, whereby the upper model calculates the energy storage allocation problem and the lower model calculates the optimal dispatch problem. The lower model’s dispatch computation validates the upper configuration model’s reasonableness. Finally, the two-layer model is converted to a single-layer model by the KKT condition, and the nonlinear problem is converted to a linear problem with the big-M method. The validity is proved via mathematical examples, and it is demonstrated that the planned energy storage plants by merchants may accomplish resource savings and mutual advantages for both users and wholesale power traders.

Pricing method of shared energy storage bias insurance service based on large number theorem

Under the goal of “double carbon”, energy storage is gradually recognized by the society as a means to adjust the output deviation of new energy sources. However, the development of energy storage industry is limited due to the high cost and unclear revenue model of energy storage batteries. Based on this, this paper proposes a business model in which shared energy storage operators provide bias insurance services for new energy sources. Firstly, based on the perspective of commercial insurance, the business model of the shared energy storage operator providing deviation insurance service is designed; secondly, based on the law of large numbers and the actuarial theory of insurance; and finally, the validity of the model is verified through calculation examples, and the feasibility of the proposed business model is also proved. The research results show that compared with the installed capacity of shared energy storage deviation insurance mode reduces 81.57 % compared with new energy storage, and the insurance cost of unit installed capacity of new energy station saves 71.07 % compared with the cost of self-built energy storage cost and deviation assessment cost, which greatly improves the energy storage utilization efficiency and the economic benefits of energy storage operators. The insurance, a financial product explored in this paper, enriches the profit model of energy storage, provides a feasible path for energy storage investors to lock in profits in advance, helps to stimulate the enthusiasm of energy storage investment, and promote the development of China's new energy and energy storage industry.

Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy

Charging electric vehicles (EV) by photovoltaics (PV) contributes to achieving carbon neutrality, but puts pressure on urban renewal, e.g., large investments in distribution grid upgrade and energy storage (ES). To solve this problem, we proposed a charging system aiming at providing intermittent but free solar charging service for private EV drivers to cover their daily intra-urban transportation demand. It is a battery-free direct-current (DC) microgrid with a distributed charging strategy, taking variable DC bus voltage as a control signal. The system was optimized and tested by simulation on an office building with 80 private EVs. We performed a system performance comparison, when achieving the same guarantee level of EV use with the parking shed fully cover by PV. A 10-kW charger (DC Level 1) with the strategy can take the role of a 3.5-kW charger (AC Level 2) with ES (26.0 kWh/parking space) to coordinate PV generation and charging load. Furthermore, connecting the system with the adjacent building can increase the annual solar self-consumption rate from 33.3% to 67.9%, and avoid the distribution grid upgrade (average 5.6 kVA/charger). The discounted payback time of this building-connected system decreases from 9.4 to 4.5 years, compared with a conventional solar charging station.

China’s role in scaling up energy storage investments

Accelerating the planning and development of a new power system that is more renewable energy-based is a strategic priority of achieving “dual carbon” goals (peaking carbon emissions before 2030 and becoming carbon neutral before 2060) in China. The large-scale development of energy storage technologies will address China’s flexibility challenge in the power grid, enabling the high penetration of renewable sources. This article intends to fill the existing research gap in energy storage technologies through the lens of policy and finance. Results indicate that policy uncertainties in renewable energy might undermine domestic investor confidence in energy storage technologies, while insufficient economic incentives may crowd out private sector participation. Drawing on international best practices, blended concessional finance, supported by development partners, can play a significant role in closing energy storage financing gaps in China and in countries of the Belt and Road Initiative (BRI). To deliver on China’s domestic and international climate commitments, this article makes three policy recommendations: (1) moving forward with a carbon pricing agenda that incentivizes energy storage investments in China; (2) tapping the potential of the domestic capital market to close financing gaps for novel energy storage technologies; (3) scaling up energy storage supply chains in BRI countries through multilateral cooperation.

Phasing out coal power in a developing country context: Insights from Vietnam

At the United Nations Framework Convention on Climate Change 26th Conference of the Parties (COP 26) in November 2021, Vietnam pledged to phase out unabated coal power by the 2040s or as soon as possible thereafter. Achieving this will require major efforts. This study investigates the drivers for Vietnam's coal power phase-out decision, barriers to Vietnam achieving a successful unabated thermal coal phase out, and potential strategies to achieve the pledge. To this end, a survey of 43 experts from government agencies, research institutions, civil society, and industry was carried out, supplemented by 23 follow-up interviews. The results indicate that ambition to attract international support for green growth initiatives in a context of limited financing options for new coal power projects appears to have been the primary driver for the decision. Key barriers include concerns about electricity shortages and incomplete regulatory frameworks for new clean power options. Recommended strategies include: 1) reforming regulations to facilitate investments in clean energy, electricity transmission, and energy storage; 2) continuing political prioritisation; and 3) building broad-based support from the community and enterprises. Vietnam's case is relevant to other developing countries and beyond.

Optimal allocation method of energy storage for integrated renewable generation plants based on power market simulation

This study designs and proposes a method for evaluating the configuration of energy storage for integrated renewable generation plants in the power spot market, which adopts a two-level optimization model of “system simulation + plant optimization”. The first step is “system simulation” which is using the power market simulation model to obtain the initial nodal marginal price and curtailment of the integrated renewable generation plant. The second step is “plant optimization” which is using the operation optimization model of the integrated renewable generation plant to optimize the charge-discharge operation of energy storage. In the third step, “system simulation” is conducted again, and the combined power of renewable and energy storage inside the plant is brought into the system model and simulated again for 8,760 h of power market year-round to quantify and compare the power generation and revenue of the integrated renewable generation plant after applying energy storage. In the case analysis of the provincial power spot market, an empirical analysis of a 1 GW wind-solar-storage integrated generation plant was conducted. The results show that the economic benefit of energy storage is approximately proportional to its capacity and that there is a slowdown in the growth of economic benefits when the capacity is too large. In the case that the investment benefit of energy storage only considers the income of electric energy-related incomes and does not consider the income of capacity mechanism and auxiliary services, the income of energy storage cannot fulfill the economic requirements of energy storage investment.

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

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