Energy Storage Investment Research Articles

Optimisation and optimal geometry design for thermal energy storages in high temperature concentrating solar power

This study presents a comprehensive decision support model formulated as a finite-horizon-constrained optimisation problem to optimally design the geometry variables that maximise the net present value (NPV) associated to the thermal energy storage (TES) investment over a given time horizon. This study faces one of the main problems in a TES, which is to react to the unpredictable production/demand processes, by determining a high-level optimal size of the TES maximising the NPV that captures the storage benefits as well as detailed fixed and variable costs over a chosen time horizon. The storage benefits are defined, so that they model the costs of the expected discharged thermal energy over a year. Moreover, the authors account for various costs model regarding the total costs of the heat transfer tube material, the storage material, and the insulation material. The proposed decision model can be considered as practical framework that can support engineers and decision makers in the process of design and planning of future. They investigate performance and efficiency of the proposed decision support system framework through representative case studies. Numerical studies demonstrate the usefulness and efficacy of the proposed decision model.

Scalable Planning for Energy Storage in Energy and Reserve Markets

Energy storage can facilitate the integration of renewable energy resources by providing arbitrage and ancillary services. Jointly optimizing energy and ancillary services in a centralized electricity market reduces the system's operating cost and enhances the profitability of energy storage systems. However, achieving these objectives requires that storage be located and sized properly. We use a bilevel formulation to optimize the location and size of energy storage systems, which perform energy arbitrage and provide regulation services. Our model also ensures the profitability of investments in energy storage by enforcing a rate of return constraint. Computational tractability is achieved through the implementation of a primal decomposition and a subgradient-based cutting-plane method. We test the proposed approach on a 240-bus model of the Western Electricity Coordinating Council system and analyze the effects of different storage technologies, rate of return requirements, and regulation market policies on energy storage participation on the optimal storage investment decisions. We also demonstrate that the proposed approach outperforms exact methods in terms of solution quality and computational performance.

Levelized cost of storage — Introducing novel metrics

The increasing share of variable renewable generation capacity leads to a growing interest in electricity storage technologies and a summarizing cost metric to analyze the economic viability of such electricity storage units. For conventional generation technologies, the levelized cost of electricity (LCOE) is a well-known metric. In the context of electricity storage however, such LCOE-like metrics are only limitedly applicable as the finite energy storage capacity can limit the charge and discharge scheduling decisions of the storage operator. In addition, the “fuel”, i.e., charged electricity, and “generated electricity”, i.e., discharged electricity, is one and the same commodity which provides the opportunity to use an adapted levelized cost metric. This work analyzes three different levelized cost metrics and their application to electricity storage units used for electric energy arbitrage. The strengths and shortcomings of these storage cost metrics are analyzed in order to determine how they can be applied correctly. This analysis results in the following recommendations. First, it is recommended to use a levelized cost metric in combination with an analysis of a representative price profile upon which the storage operator will act. This allows a more accurate estimation of the number of charging and discharging hours and the associated charging cost and discharging revenue, given the energy storage capacity constraints of the storage unit. Second, when a number of different representative price profiles, hence with different charging costs, is available, it is recommended to use a cost metric which is independent of the charging cost as this single metric can be compared to each price profile, thereby facilitating the interpretation of the results. The results and conclusions from this work provide a framework on how to use levelized cost metrics in the context of electricity storage. Such metrics may help policy makers and investors in prioritizing energy storage investment decisions.

Ensuring Profitability of Energy Storage

Energy storage (ES) is a pivotal technology for dealing with the challenges caused by the integration of renewable energy sources. It is expected that a decrease in the capital cost of storage will eventually spur the deployment of large amounts of ES. These devices will provide transmission services, such as spatiotemporal energy arbitrage, i.e., storing surplus energy from intermittent renewable sources for later use by loads while reducing the congestion in the transmission network. This paper proposes a bilevel program that determines the optimal location and size of storage devices to perform this spatiotemporal energy arbitrage. This method aims to simultaneously reduce the system-wide operating cost and the cost of investments in ES while ensuring that merchant storage devices collect sufficient profits to fully recover their investment cost. The usefulness of the proposed method is illustrated using a representative case study of the ISO New England system with a prospective wind generation portfolio.

Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards

Worldwide, environmental regulations such as Renewable Portfolio Standards (RPSs) are being broadly adopted to promote renewable energy investments. With corresponding increases in renewable energy deployments, there is growing interest in grid-scale energy storage systems (ESS) to provide the flexibility needed to efficiently deliver renewable power to consumers. Our contribution in this paper is to introduce a unified generation, transmission, and bulk ESS expansion planning model subject to an RPS constraint, formulated as a two-stage stochastic mixed-integer linear program (MILP) optimization model, which we then use to study the impact of co-optimization and evaluate the economic interaction between investments in these three asset classes in achieving high renewable penetrations. We present numerical case studies using the 24-bus IEEE RTS-96 test system considering wind and solar as available renewable energy resources, and demonstrate that up to $180million/yr in total cost savings can result from the co-optimization of all three assets, relative to a situation in which no ESS investment options are available. Surprisingly, we find that co-optimized bulk ESS investments provide significant economic value through investment deferrals in transmission and generation capacity, but very little savings in operational cost. Finally, we observe that planning transmission and generation infrastructure first and later optimizing ESS investments—as is common in industry—captures at most 1.7% ($3million/yr) of the savings that result from co-optimizing all assets simultaneously.

Distribution feeder capacity augmentation and efficiency improvement through energy storage

The distribution feeders often get overloaded when power demand is at peak. This situation is generally countered by load shedding, which leads to financial loss to utilities and individual users. The compensation of real/reactive power not only provides the voltage profile improvement, but also ensures the sustain power to the consumers. This paper presents the new control algorithm to control the real and reactive load power demand through energy storage (ES). The efficiency function, which co-relates to the thermal limit and storage energy availability, is integrated into the feeder capacity augmentation. This control also maintains the unity power factor on the source side which ensures the relief from the reactive power support from the source. These distribution networks with available extra capacity for real power transfer are exploited up to a maximum thermal limit. The available surplus capacity can be used for more customers which justifies the additional energy storage investment. The operational features are presented through a prototype model developed in a laboratory. The financial analysis is also presented to justify the energy storage for a given distribution network.

Distribution feeder capacity augmentation and efficiency improvement through energy storage

The distribution feeders often get overloaded when power demand is at peak. This situation is generally countered by load shedding, which leads to financial loss to utilities and individual users. The compensation of real/reactive power not only provides the voltage profile improvement, but also ensures the sustain power to the consumers. This paper presents the new control algorithm to control the real and reactive load power demand through energy storage (ES). The efficiency function, which co-relates to the thermal limit and storage energy availability, is integrated into the feeder capacity augmentation. This control also maintains the unity power factor on the source side which ensures the relief from the reactive power support from the source. These distribution networks with available extra capacity for real power transfer are exploited up to a maximum thermal limit. The available surplus capacity can be used for more customers which justifies the additional energy storage investment. The operational features are presented through a prototype model developed in a laboratory. The financial analysis is also presented to justify the energy storage for a given distribution network.

A Two-Part Tariff Model for Energy Intermediation

The proliferation of intermittent and distributed sources of power generation leads to potential instability of the utility grid. The unforecastability of weather patterns, cloud cover, and the like and the consequent unpredictability of output from such sources as wind farms and solar power plants aggravate the situation created by unpredictable consumer demand for power. Energy storage has been increasingly seen as a crucial element in resolving the challenge and several electrical, chemical, and mechanical systems have been analyzed. On occasion, storing a unit of electric power could cost more than generating one. This article argues that investments in energy storage should be justified on a stand-alone basis. Trading margins alone are found insufficient to justify economic investments in storage facilities. To sustain investor interest in the long run, such facilities would have to bank and trade energy along the lines of mainstream commercial banks and earn economic profits comparable to alternative applications of funds. This article discusses an economic model involving a two-part tariff: an option price and a trading margin from energy time-shifting.

Power to Gas: The Final Breakthrough for the Hydrogen Economy?

AbstractIn Germany more than 20% of the energy mix is made up of renewable energy and its share is rapidly increasing. The federal government expects renewables to account for 35% of Germany's electricity consumption by 2020, 50% by 2030 and 80% by 2050. According to the German Energy Agency, multi-billion euro investments in energy storage are expected by 2020 in order to reach these goals. The growth of this fluctuating energy supply has created demand for innovative storage options in Germany and it is accelerating the development of technologies in this field. Along with batteries and smart grids, hydrogen is expected to be one of the lead technologies. 2010 a commercialization roadmap for wind hydrogen was set up by the two northern federal states of Hamburg and Schleswig-Holstein with the goal of utilizing surplus wind power for the electrolytic production of hydrogen. With the creation of the “performing energy initiative”, 2011, Brandenburg and Lower Saxony joined this undertaking. The aim of this initiative is to set up demonstration projects in order to develop and optimize wind-hydrogen hybrid systems and prepare their commercialization for the time after 2020. Beside the conversion of hydrogen into electricity and fuel for cars, further markets like raw material for the chemical, petrochemical, metallurgy and food industry are going to be addressed. Considering the fact there are over 40 caves currently used for natural gas storage with a total volume of 23.5 billion cubic meters and 400 000 km gas grid available in Germany, the German Technical and Scientific Association for Gas and Water sees opportunities for hydrogen to be fed into the existing natural gas grid network. The name of this concept is power-to-gas. According to the current DVGW-Standards natural gas in Germany can contain up to 5% hydrogen. The GERG, European Group on the Gas Research sees potential to increase this amount up to 6% to 20%. Power-to-gas could serve both for fuel and for the storage of extra energy produced by renewable sources. The hydrogen produced via electrolysis could be drawn upon – directly or as synthetic natural gas (SNG) in a second additional methanation process step – to provide electricity by means of CCGT (combined cycle gas turbines) or CHP (combined heat and power) using for example fuel cells. It could also address the industrial and household heat market. DVGW is furthermore participating in the “Power-to-Gas Platform” that was set up in 2012 by the German Energy Agency, bringing together RnD institutes, renewable energy project developers and park operators, utilities, underground storage providers in order to create political support for this new technology. Demonstration projects will be completed by 2020 in order to develop business models (for storage, production and trade of “green gas”) and devices (electrolysers, turbines, smart gas metering, compressors, storage capacities amongst others) to enable the implementation of this concept on a broad scale. This means that a multitude of industrial players will be involved in the changes that will occur in the value chain: utilities (electricity, gas), power technology companies, car makers, heating device manufacturers, but also manufacturers of measurement, regulation and control devices, suppliers of the biogas and methanation industry. Germany is the pioneer in this field. This technology however increasingly interests its neighbours, with project developments in France, Italy, Spain, and UK but also in North America and North Africa. Germany can contribute its valuable experience (e.g. legal framework for power-to-gas) to the development of these industries. German participants in demonstration projects in these countries could for example be renewable energy park operators, RnD institutes and suppliers.

Optimal day-ahead trading and storage of renewable energies—an approximate dynamic programming approach

A renewable power producer who trades on a day-ahead market sells electricity under supply and price uncertainty. Investments in energy storage mitigate the associated financial risks and allow for decoupling the timing of supply and delivery. This paper introduces a model of the optimal bidding strategy for a hybrid system of renewable power generation and energy storage. We formulate the problem as a continuous-state Markov decision process and present a solution based on approximate dynamic programming. We propose an algorithm that combines approximate policy iteration with Least Squares Policy Evaluation (LSPE) which is used to estimate the weights of a polynomial value function approximation. We find that the approximate policies produce significantly better results for the continuous state space than an optimal discrete policy obtained by linear programming. A numerical analysis of the response surface of rewards on model parameters reveals that supply uncertainty, imbalance costs and a negative correlation of market price and supplies are the main drivers for investments in energy storage. Supply and price autocorrelation, on the other hand, have a negative effect on the value of storage.

Compressed Air Energy Storage in an Electricity System With Significant Wind Power Generation

In this paper, a stochastic electricity market model is applied to estimate the effects of significant wind power generation on system operation and on economic value of investments in compressed air energy storage (CAES). The model's principle is cost minimization by determining the system costs mainly as a function of available generation and transmission capacities, primary energy prices, plant characteristics, and electricity demand. To obtain appropriate estimates, notably reduced efficiencies at part load, start-up costs, and reserve power requirements are taken into account. The latter are endogenously modeled by applying a probabilistic method. The intermittency of wind is covered by a stochastic recombining tree and the system is considered to adapt on increasing wind integration over time by endogenous modeling of investments in selected thermal power plants and CAES. Results for a German case study indicate that CAES can be economic in the case of large-scale wind power deployment

Somatic energy storage and reproductive investment in the grass goby Zosterisessor ophiocephalus

The pattern of somatic energy storage, measured as body lipid proportion, was investigated in the grass goby Zosterisessor ophiocephalus, by comparing the per cent lipids across three reproductive groups—large males (territory holding, caring for eggs, males), small males (sneaker males) and females—and across a seasonal phase including the entire breeding season and a post-reproductive period. Furthermore, the pattern of some indicators of somatic and reproductive investment, such as gonadosomatic index (GSI) and liversomatic index (LSI), was investigated, as well as the relationship between lipid content and fish body size was studied over time in both sexes. Results showed that the amount of somatic lipids depends largely on the different levels of reproductive investment shown by the breeding individuals. Females had the lowest levels of somatic lipids at the beginning of the breeding season when large males reached, by contrast, the highest levels. Large males showed a strong decrease in their reserves as the breeding season proceeded, and their replenishment was significantly slower than that of smaller males, which had relatively high levels already in September. Analysis of the trends of GSI and LSI revealed that the difference between females and males in the primary reproductive investment (gonadic mass) can explain most of the differences in the levels of somatic energy storage. The relationship between body size and lipid content changed over time, being positive and statistically significant at the beginning of the breeding season for each reproductive group, indicating that during these phases all the breeding individuals had accumulated energy in their tissues and that the breeding season reduced their somatic energy storage although with different intensity.