Energy Storage Resources Research Articles

Evaluating Potential Benefits of Flexible Solar Power Generation in the Southern Company System

As penetration of variable renewable power increases, the power system needs more flexibility to manage the variability and uncertainty in the operation process. Allowing solar generation to provide this flexibility can bring substantial benefits to the system. This article evaluates the impacts of flexible solar power generation in the Southern Company power system. The production cost model simulation methodology was used for the analysis, with a 5-min dispatch temporal resolution in the real-time. Enabling a subhourly production cost simulation helps to capture more variability in system load and renewable generation. The solar and wind generation forecasting data were simulated with a realistic nature. A total of 15 scenarios were tested with solar capacities of 2, 7, and 20 GW respectively. Simulation results show that the system may see operational challenges to balance the load and meet the reserve requirements with increased solar penetration level and reduced flexibility due to the retirement of coal plants. Enabling solar power generation flexibility will bring significantly benefits to the system, including reduced penalty costs, increased reliability, reduced solar curtailment, and reduced greenhouse gas emissions. The results also show that energy storage can provide some of the same grid services as flexible solar, thereby reducing the value of flexible solar. Nevertheless, combining flexible solar and energy storage resources could bring additional benefits to the system.

Locational Tariff Structure for Radial Network Fixed Costs in a DER Context

The regulation of Distributed Energy Resources (DERs) has cast doubt on the sustainability of utility infrastructure charging models. The development of feed-in tariffs, net-metering, and network charge rebates for distributed generation (DG) has been questioned because of the cross-subsidies that result between passive consumers and DG investors. Besides DG investors, other new entrants, such as owners of energy storage resources and electrical vehicles, are creating challenges for the regulation of distribution service pricing as a whole. This paper proposes a new approach to dealing with the fixed cost element of service pricing, that enhances economic signaling in the distribution network. We isolate and address the issue from a distribution network point of view, by excluding from our model the random variables associated with the complexity of social, environmental and other externalities. The solution developed is particularly relevant at a time when distribution networks, historically dominated by passive users, struggle to adapt to a dramatic increase in the number of users who are active agents. It considers the principles governing tariff design from the perspective of simplicity, economic signaling, and revenue reconciliation. Results are presented of simulations performed with different arrangements of alternative energy generators and energy storage devices, using an actual feeder from a distribution company in Brazil. An analysis of these results is provided that suggests a combination of locational and time-of-use rates can provide effective economic signals to these new types of system user.

Research on Electricity Retail Market Design Considering Renewable Energy Storage Resources

This research focus on electricity retail market design considering renewable energy storage resources under the background of the National Development and Reform Commission issuing the policy on establishing and improving the consumption and consumption guarantee of renewable energy power. Renewable energy storage resources are the key consideration in the design of current market mechanism problems. This research shows the investment and construction costs of renewable energy storage resources based on the analysis of the electricity retail market. The research results provide practical reference for electricity retail market players to design power retail packages that meet clean energy consumption in a diversified and competitive environment.

Adaptation of Temperate Seagrass to Arctic Light Relies on Seasonal Acclimatization of Carbon Capture and Metabolism.

Due to rising global surface temperatures, Arctic habitats are becoming thermally suitable for temperate species. Whether a temperate species can immigrate into an ice-free Arctic depends on its ability to tolerate extreme seasonal fluctuations in daylength. Thus, understanding adaptations to polar light conditions can improve the realism of models predicting poleward range expansions in response to climate change. Plant adaptations to polar light have rarely been studied and remain unknown in seagrasses. If these ecosystem engineers can migrate polewards, seagrasses will enrich biodiversity, and carbon capture potential in shallow coastal regions of the Arctic. Eelgrass (Zostera marina) is the most widely distributed seagrass in the northern hemisphere. As the only seagrass species growing as far north as 70°N, it is the most likely candidate to first immigrate into an ice-free Arctic. Here, we describe seasonal (and diurnal) changes in photosynthetic characteristics, and in genome-wide gene expression patterns under strong annual fluctuations of daylength. We compared PAM measurements and RNA-seq data between two populations at the longest and shortest day of the year: (1) a Mediterranean population exposed to moderate annual fluctuations of 10–14 h daylength and (2) an Arctic population exposed to high annual fluctuations of 0–24 h daylength. Most of the gene expression specificities of the Arctic population were found in functions of the organelles (chloroplast and mitochondrion). In winter, Arctic eelgrass conserves energy by repressing respiration and reducing photosynthetic energy fluxes. Although light-reactions, and genes involved in carbon capture and carbon storage were upregulated in summer, enzymes involved in CO2 fixation and chlorophyll-synthesis were upregulated in winter, suggesting that winter metabolism relies not only on stored energy resources but also on active use of dim light conditions. Eelgrass is unable to use excessive amounts of light during summer and demonstrates a significant reduction in photosynthetic performance under long daylengths, possibly to prevent photoinhibition constrains. Our study identified key mechanisms that allow eelgrass to survive under Arctic light conditions and paves the way for experimental research to predict whether and up to which latitude eelgrass can potentially migrate polewards in response to climate change.

Optimal planning of a hybrid system integrating of combined cooling, heat and power and energy storage resources

The Energy Storage System (ESS) can address the difference in the ratio of heat to electricity among a combined cooling, heating, power (CCHP) system and its customers, and the ESS can progress energy efficiency. Due to various limitations of this model, the ESS raises the complexity of performance of optimizing system. It is reasonable to employ a powerful tool to solve this problem, thus, this paper proposes an improved hybrid Virus Colony Search (VCS) with the Genetic Algorithm (GA) method. The combination of these two VCS and GA methods has significantly improved the local and global searching process. To evaluate the proposed algorithm and planning problem, a test system is selected with various operating conditions in 24 h. The simulation results shown that the overall performance by using of proposed hybrid algorithm increases by 1.93% in summer and 1.92% in winter as compared to other methods. Also, the numerical results demonstrated that there are acceptable performance in both the computing time and the optimal solution. The successful optimization algorithm for day-ahead and real-time planning problems is integrated the CCHP system with TES having demand-side response.

Capacity allocation and pricing for energy storage sharing in a smart community

The increasing energy storage resources at the end-user side require an efficient market mechanism to facilitate and improve the utilization of energy storage (ES). Here, a novel ES capacity trading framework is proposed for ES sharing of a smart community consisting of multiple ES owners (ESOs) and users. Specifically, an iterative clearing approach is designed to capture the interaction between the ESOs and users. Players submit trading parameters with consideration of their own preferences and profiles according to utility functions. After receiving the offering/bidding parameters, the market operator determines the capacity allocation and clearing price based on the principle of maximizing social welfare. This decentralized clearing process avoids complex calculation induced by the centralized decision method and preserves players’ privacy. Case studies are conducted to confirm the effectiveness of the proposed scheme. The results show that ES sharing scheme brings additional revenues to ESOs, decreases costs for users, and reduces the peak-to-valley difference of system loads. In addition, the clearing results are very close to global optimal solutions and mainly affected by the maximum capacity and reservation price of each ESO.

Adaptive Droop Coefficient and SOC Equalization-Based Primary Frequency Modulation Control Strategy of Energy Storage

In order to efficiently use energy storage resources while meeting the power grid primary frequency modulation requirements, an adaptive droop coefficient and SOC balance-based primary frequency modulation control strategy for energy storage is proposed. Taking the SOC of energy storage battery as the control quantity, the depth of energy storage output is adaptively adjusted to prevent the saturation or exhaustion of energy storage SOC. The balanced control strategy is introduced to realize the rational utilization of resources and the fast balance of SOC in the process of primary frequency modulation of energy storage battery under different charge states. Then, four evaluation indexes are proposed to evaluate the effect of primary frequency modulation and SOC maintenance. Taking a regional power grid as an example, a simulation analysis is carried out under step load disturbance and continuous load disturbance. According to the simulation results, the proposed control strategy is effective in power system frequency regulation and battery SOC maintenance.

A robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve markets

The high reliability and flexibility of Battery Energy Storage (BES) resources in comparison with other renewable technologies promote the development of this technology in smart grids. The fast response of BES to load variations could help the power system operators to maintain the balance of generation and consumption in real-time, and improve the flexibility of the smart grid, effectively. In this work, a new model is presented that determines the aggregated scheduling of BES and Wind Power Resource (WPR) in the joint energy and reserve markets. To evaluate the performance of BES in different markets, the proposed model is divided into day-ahead and real-time planning horizons. According to market prices, ramp rates, marginal costs, and technical constraints of units, the optimal participation levels in different markets are determined. The deployed power in real-time and wind power are considered as the uncertain parameters and the Robust Optimization (RO) framework is proposed to manage the related financial risk based on the worst-case realizations of uncertain parameters. The robust strategy is formulated based on the Mixed Integer Linear Programming (MILP) technique, which can be solved via the branch-and-bound method. Finally, the performance and effectiveness of the model are analyzed via different case studies. Simulation results show that the day-ahead and real-time markets are the best options for buying and selling the energy of BESs, and participation in the reserve market and regulation service increases their profit, significantly. Furthermore, the expected profit greatly depends on the risk preferences of decision-makers, and reducing the variation interval of wind generation by 40 % leads to an increase of 74.65 % in revenues.

The importance of temporal resolution in modeling deep decarbonization of the electric power sector

Power sector decarbonization is a central pillar of economy-wide emissions reductions. However, model complexity, especially temporal resolution, can materially impact power sector decarbonization pathways. Using a detailed electric sector capacity planning and dispatch model, this analysis explores impacts of temporal resolution on electric sector investments and costs and how these outcomes vary under different policy and technology assumptions. Results show that approaches to simplify temporal variability used in many integrated assessment and energy system models may not replicate fundamental relationships for power sector decarbonization or may exhibit large quantitative deviations from more detailed modeling, including abatement costs rising nonlinearly at higher decarbonization levels; variable renewables and batteries being accompanied by additional low-/zero-/negative-emissions resources, especially approaching 100% decarbonization; and carbon removal technologies altering electric sector costs and investments. Representative day approaches can preserve many of these properties with large reductions in computational complexity. Simplified temporal aggregation approaches tend to understate the value of broader technological portfolios, firm low-emitting technologies, wind generation, and energy storage resources and can overstate the value of solar generation. Approximation accuracy also depends on assumptions about technological cost and availability: differences across approaches are smaller when carbon removal is available and when renewables costs are lower. The analysis indicates that higher temporal resolution is increasingly important for policy analysis, electric sector planning, and technology valuation in scenarios with deeper decarbonization and higher variable renewables.

Evaluation model of key driving factors for different types of demand side distributed power resources to participate in market transactions

Customer side distributed power resources are of great significance for low-cost guarantee of supply and demand balance and system peak shaving, and participation in market transactions is conducive to reducing project operation costs. Connotation and types of customer side comprehensive resources are analyzed, constructing the driving factor index system for customer side resources to participate in market transactions, including three indicators: internal rate of return of project investment, cost performance of participating in transactions and meeting their own electricity demand, and the perfection of supporting policies for participating in transactions, and evaluating the key driving factors for current relatively mature gas-fired CCHP, distributed photovoltaic power generation, electrochemical energy storage and demand side response resources to participate in market trading, putting forward the implementation path suggestions, promoting the large-scale and normalized trading of qualified customer side resources.

Aggregating Distributed Energy Storage: Cloud-Based Flexibility Services From China

To meet the newest carbon emission reduction and carbon neutrality targets, the capacity of variable renewable energy sources in China is planned to double in the next five years. A high penetration of renewable energy brings significant power system flexibility challenges, and the requirements for flexible resources become increasingly critical. Energy storage, as an effective and adaptable solution, may still be too expensive for peak shaving and renewable energy integration. A new type of business model has been proposed that uses cloud-based platforms to aggregate distributed energy storage resources to provide flexibility services to power systems and consumers. In such cloudbased platforms, storage resources can be more strategically used so that the unit cost of providing the service can be reduced. In the actual implementation of cloud-based energy storage, distributed resources have also been expanded from batteries to a variety of solutions, such as variable loads and heat storage.

Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm

Increasing energy demand, energy generation costs, and environmental concerns are among the reasons driving governments to develop technologies in order to save energy and reduce greenhouse gas emissions. The combined cooling, heating, and power systems are among the technologies developed to reduce primary energy consumption, costs, and greenhouse gas emissions. Combined cooling, heating, and power systems generate power on-site by a primary actuator. The difference between these systems and traditional power plants is that these systems use the heat dissipated from the primary actuator to meet consumers’ heating and cooling demands. These systems convert the waste heat in the power generation sector into the heating and cooling energy required by users to improve performance. This study aims to develop a multi-objective model considering three objective functions in energy, economic, and environmental areas as well as different practical and nonlinear constraints to effectively implement this system in the power grid. Given that these functions are inherently contradictory, an optimized multi-objective grey wolf algorithm is proposed as a solution. The proposed multi-objective algorithm is a model developed based on non-dominated sorting theory, variable detection, memory-based strategy selection, and fuzzy theory to select the optimal Pareto from the set of solutions, which has powerful performance in solving the above problem. The results obtained from the figures and tables in different time periods indicated the proposed method increased by about 2% in summer and 2% in winter compared to other methods. In addition, the computation time for energy system planning was acceptable.

Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning

Most utilities across the world already have demand response (DR) programs in place to incentivise consumers to reduce or shift their electricity consumption from peak periods to off-peak hours usually in response to financial incentives. With the increasing electrification of vehicles, emerging technologies such as vehicle-to-grid (V2G) and vehicle-to-home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, electric vehicles (EV) become distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Here, an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) is proposed. Q-learning, an RL strategy based on a reward mechanism, is used to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue.

Повышение экономических показателей эксплуатации атомных ледоколов при проводке судов в Арктике

The most expensive part of nuclear icebreakers to be replaced is the reactor core, and therefore great importance was attached to increasing its energy storage and energy resource. At the first stage of the active zone development, it was possible to solve the problem of equalizing the calculated energy storage and the assigned energy resource. Another problem related to the icebreaker maneuvering in the Arctic is the unreasonably overestimated power limitation of the electric propulsion installation set by the navigator, leading to a decrease in the duration of the core campaign and its premature unloading from the reactor. The article proposes solutions that allow increasing the duration of the active zone campaign in the conditions of icebreaker maneuvering in the Arctic.

An economy and reliability co-optimization planning method of adiabatic compressed air energy storage for urban integrated energy system

The improving energy supply reliability requirements pose significant challenges to the energy storage planning in the urban integrated energy system (UIES). Traditional energy storage planning methods usually only take the economy optimization as the objective. These methods have difficulty meeting the requirement of reliability. The co-optimization of economy and reliability has been regarded as a new target. However, this new target is difficult to be achieved because of the complexity of energy storage resources in UIES and the nonlinearity of mathematical formulation to express the reliability. Therefore, how to achieve the maximum comprehensive improvement of economy and reliability within the limited scope of planning resources is a key issue that needs to be resolved. Adiabatic compressed air energy storage (A-CAES) has the capability of combined cooling, heating and power supply. The incorporation of A-CAES in UIES can improve the system economy and reliability effectively. To this end, a reasonable bi-level optimal planning method for A-CAES combined with heat/cold energy storage is proposed considering the economy and reliability co-optimization. Firstly, the mathematical formulations of economy and reliability objectives are presented separately. To unify the two planning objects, the reliability object is transformed from the minimum outage capacity into the minimum outage penalty cost, which is regarded as the reliability cost. The reliability cost and economy cost are integrated as the objective function of the planning level. Then, based on the rated power and capacity of A-CAES obtained from the planning level, the optimal scheduling model of UIES is established for evaluating the annual system operation cost and reliability cost. Finally, a UIES containing an A-CAES is used for simulation analysis. The simulation results show that the proposed model can achieve the comprehensive optimization of economy and reliability.

The role of hydropower reservoirs in deep decarbonization policy

This paper analyzes the role of hydropower reservoirs in the deep decarbonization of power systems. Extending previous work, this study models the impact of hydro reservoirs on optimal planning decisions. It further provides a more holistic assessment of the role and economic value of hydro reservoirs through the use of a detailed capacity expansion and dispatch model. Our model is parameterized based on the power system of New England, U.S. and the hydro-based system of Quebec, Canada. We find that expanding transmission access to hydro reservoirs reduces the need for fossil-fuel power plants that may otherwise be deployed to balance renewable intermittency. Our results show that hydro access can accelerate decarbonization by decreasing optimal gas plant capacity and generation. At levels of very deep decarbonization, reservoir hydro reduces the need for Carbon Capture and Storage. Our modeling shows how hydro reservoirs accomplish this by serving as both a short- and long-term energy storage resource. We further show that, by reducing the need for more expensive balancing technologies and by enabling a more efficient utilization of variable renewables, hydro access lowers the cost of decarbonization, and that this benefit grows non-linearly with the decarbonization target.

Reliability evaluation of regional energy Internet considering electricity–gas coupling and coordination between energy stations

In view of the issues that the current comprehensive energy supply reliability evaluation mainly focuses on the distributed energy system and rarely involves the mutual coupling of energy supply networks and the coordination of energy stations in the regional energy Internet, an evaluation method of energy supply reliability considering electricity–gas coupling and coordination between energy stations was proposed. First, the framework of regional energy Internet (REI) is established, the energy flow relationship among the main parts in the REI and the influence of electricity–gas coupling on reliability are analysed; second, considering building virtual energy storage and demand response, the rigid and flexible reliability evaluation indexes are proposed from the energy and time levels; third, the operation strategies of normal, failure and repair periods of each energy station are put forward, and the optimal load reduction model of REI aiming at the minimum total rigid energy shortage is established. Moreover, the method and process of energy supply reliability evaluation combining fault mode effect analysis (FMEA) and Monte Carlo simulation are proposed. Finally, through a practical example, the impact of demand response, multiple energy storage resources, electricity–gas coupling and inter-station coordination on reliability is analysed.

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

Large-scale renewable energy sources connected to the grid bring new problems and challenges to the automatic generation control (AGC) of the power system. In order to improve the dynamic response performance of AGC, a biobjective of complementary control (BOCC) with high-participation of energy storage resources (ESRs) is established, with the minimization of total power deviation and the minimization of regulation mileage payment. To address this problem, the strength Pareto evolutionary algorithm is employed to quickly acquire a high-quality Pareto front for BOCC. Based on the entropy weight method (EWM), grey target decision-making theory is designed to choose a compromise dispatch scheme that takes both of the operating economy and power quality into account. At last, an extended two-area load frequency control (LFC) model with seven AGC units is taken to verify the effectiveness and the performance of the proposed method.

The Federation: Enabling interregional electric transmission development and operation

The electric utility industry in the United States continues to seek lower costs by employing increasing amounts of low-cost wind and solar energy. Some states are continuing to require utilities to increase the use of renewable energy in accordance with governmental policy.But as solar and wind resources become an increasingly important share of the power supply portfolio, the result can be serious negative operational impacts on system reliability. Some systems have begun to experience rolling backouts as a result.11Sammy Roth, California Blackouts are Public Utilities’ Commission’s Fault, Grid Operator Says, The Los Angeles Times, August 17, 2020. These rotating blackouts of course create high levels of customer dissatisfaction.Such loss of reliability and customer satisfaction must be addressed. Society will accept neither rotating blackouts nor significantly higher costs. Meanwhile, it still wants 100 % clean energy. How to reconcile these three goals at the same time? The problem is not the increasing levels of renewables. It is just that our utility business models have not evolved to effectively take advantage of them as the industry continues to use renewables to displace fossil-fueled resources.This article describes a new, innovative, and productively disruptive business and operating model that can enable high voltage direct current (HVDC) interregional transmission and the “macrogrid” it represents. Together, they can address the reliability shortfalls by improving the contributions to reliable generating capacity of ever-increasing levels of wind and solar while preserving their economic and environmental benefits.Founded on the principles of continued service reliability, lower costs and lower carbon emissions, the new model does not require a cogent federal energy policy (although that would certainly be useful) or the merging of current Regional Transmission Organization (RTO) or Independent System Operator (ISO) wholesale markets.The approach requires a new business model to position the participants for success in this changing market. It uses a small number of utilities, developers and states voluntarily joining together as coalitions of the willing in their own mutual self-interest. Using interregional HVDC transmission, it would aggregate at the wholesale level widely dispersed, time-diversified renewable energy sources, combine them with energy storage and other firming resources, and offer renewable energy products with various specific levels of reliability and associated prices to the wholesale markets. It would redistribute renewable energy surpluses and deficits caused by their otherwise non-dispatchable intermittency and make their aggregated capacity more reliable.The new business model is called the Federation.

The pure PV-EV energy system – A conceptual study of a nationwide energy system based solely on photovoltaics and electric vehicles

The objective of this conceptual study is to reveal the substantial potential and synergy of solar energy and electric vehicles (EVs) working together. This potential is demonstrated by studying the feasibility of a nationwide energy system solely reliant on solar energy and EVs. Photovoltaic (PV) solar energy is already an important energy source globally, but due to its intermittency it requires energy storage to balance between times of high and low production. At the same time, a global drive is underway in the transport sector: the change from internal combustion engines to EVs. Cars are in fact stationary 95% of the time, and when the vehicle is connected to the grid, the EV battery can regulate the intermittent PV source using vehicle-to-grid (V2G) technology. This paper presents a conceptual study of a pure PV-EV based energy system, with Spain as a case study. Provided that Spain’s entire fleet of 29.4 million road going vehicles is switched to EVs, the study shows that 3.45 billion m2 of PV (73 m2 per capita) could give Spain a completely self-reliant energy system. The theoretical study is based on a combination of measured values, simulations, and assumptions. The conclusion of the analysis is undoubtedly extraordinary, namely that an entire country like Spain can power its complete energy system solely on PV, using EVs as the only energy storage resource.