Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a battery’s fast ramping capability. However, battery life might be decreased by frequent charge–discharge cycling, especially when providing fast regulation service. It is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model. This novel bidding model would help investor-owned battery storages better decide their bidding and operational schedules and investors to estimate the battery storage’s economic viability. The validity of the proposed model is proven by case study results.

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Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life

This paper presents a model for the microgrid planning problem with uncertain physical and financial information. The microgrid planning problem investigates the economic viability of microgrid deployment and determines the optimal generation mix of distributed energy resources (DERs) for installation. Net metering is considered for exchanging power with the main grid and lowering the cost of unserved energy and DER investments. A robust optimization approach is adopted for considering forecast errors in load, variable renewable generation, and market prices. The microgrid islanding is further treated as a source of uncertainty. The microgrid planning problem is decomposed into an investment master problem and an operation subproblem. The optimal planning decisions determined in the master problem are employed in the subproblem to examine the optimality of the master solution by calculating the worst-case optimal operation under uncertain conditions. Optimality cuts sent to the master problem will govern subsequent iterations. Numerical simulations exhibit the effectiveness of the proposed model and further analyze the sensitivity of microgrid planning results on variety levels of uncertainty.

Microgrid Planning Under Uncertainty

Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.

Real-Time Energy Management in Microgrids

The global effort to decarbonise electricity systems has led to widespread deployments of variable renewable energy generation technologies, which in turn has boosted research and development interest in bulk Electrical Energy Storage (EES). However despite large increases in research funding, many electricity markets with increasingly large proportions of variable renewable generation have seen little actual bulk EES deployment. While this can be partly attributed to the need for technological developments, it is also due to the challenge of fairly rewarding storage operators for the range of services that storage provides to the wider network, especially in markets that have undergone significant restructuring and liberalisation. Pumped Hydroelectric Energy Storage (PHES) is the overwhelmingly established bulk EES technology (with a global installed capacity around 130 GW) and has been an integral part of many markets since the 1960s. This review provides an historical overview of the development of PHES in several significant electrical markets and compares a number of mechanisms that can reward PHES in different international market frameworks. As well as providing up-to-date information about PHES, a primary motivation for this work is to provide an overview about the types of rewards available to bulk EES for the wider storage community including investors, technology developers and policy-makers. Observing that bulk EES projects seem to be unattractive investments for the private sector, the paper also includes a brief discussion in terms of public sector investment.

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A review of pumped hydro energy storage development in significant international electricity markets

The most important impact of power electronics on our society in the last 50 years has been the elimination of the 60-Hz ac power delivery system for consumer electronic products. Central to this achievement is the use of silicon (Si) power devices and pulsewidth modulation (PWM) techniques in delivering regulated ac and dc powers to low-voltage (LV) loads such as light-emitting diodes and computers. These solid-state power electronic converters have provided our society numerous benefits, including high-quality power and substantial energy savings. They also form the core technology for integrating renewable energies such as wind and solar into our power grid. Figure 1 shows a typical power delivery architecture commonly found in computer supplies and data centers. The incoming universal ac grid power is converted by a power factor correction circuit to 400 V dc before it is stepped down to a lower voltage dc intermediate bus, such as 12 V, and then it powers the digital loads at voltages as low as 1 V by a point-of-load converter. Si power metal-oxide-semiconductor field-effective transistor (MOSFET) transistors from 20 V to 700 V are almost exclusively used in this application with switching frequencies from tens of kilohertz to one megahertz. Emerging devices based on gallium nitride (GaN) heterojunction field effect transistors reduce the switching and conduction losses when compared with Si power MOSFETs and are, therefore, poised to compete in these applications, driven by the need for higher energy efficiency and higher power density.

Medium-Voltage Solid-State Transformer: Technology for a Smarter and Resilient Grid

With the increased environmental concern, the photovoltaic (PV) generation capacity is growing in today’s power systems. As the PV penetration rate increases, the intermittency and uncertainty of PV systems will cause frequency regulation issues. When rapid fluctuations take place, the system requires fast responding regulation to recover the frequency within a short period of time. Traditional power plants with slow dynamics are less capable of tracking the fast-changing regulation signal. In this context, a battery energy storage system (BESS) is considered as an effective regulation source to respond immediately to frequency deviations. This paper addresses the sizing issue of an aggregated BESS by a series of system level performance tests with different BESS penetration rates. The evaluation criteria are the control performance standards 1 and 2. Response effectiveness of the BESS with different levels of disturbances is also analyzed, with comparison to that of the traditional power plants. The proposed BESS aggregation controller is also validated using software simulations and a hardware testbed.

Penetration Rate and Effectiveness Studies of Aggregated BESS for Frequency Regulation

In this paper, we present optimal day-ahead scheduling and control models for microgrids under uncertainty. The traditional deterministic mixed integer programming used in wholesale market fails to produce adequate results, especially when variations and risks are high. We develop stochastic programming optimization models with risk neutral and risk-averse options. We also characterize microgrids with their mean design capacity configuration and operational volatility. A dissimilarity measure determines the relative volatility between two microgrids. We demonstrate that risk-based decision-making in microgrids can significantly change the day-ahead scheduling and control outcomes, and capacity configuration and volatility measures are determining factors for microgrid risks and savings.

Toward Optimal Day-Ahead Scheduling and Operation Control of Microgrids Under Uncertainty

Energy storage is a novel technology with perceived performance and lifecycle risks. In addition, there are many different business/regulatory paradigms for investors in storage resources based on existing business models for electric power assets today. At the heart of designing storage applications for best cost-benefit results and for evaluating storage economics of particular applications is the way different business models realize direct monetized benefits and indirect avoided costs from energy storage. This paper unpacks the complexities of deploying and operating energy storage and identifies any potential barriers to participation in storage. It lays out some of the existing and hypothetical business models for the investment in and operation of electric storage, and explores the complexities and possibilities of these business models when storage is bucketed” as a generation, transmission, distribution, or customer asset. It also identifies possible policy recommendations and areas for further analysis, including some alternative models that consider storage as a unique asset class that offers services offered by other assets today.

Business Models for Deploying and Operating Energy Storage and Risk Mitigation Aspects

In this article, we demonstrate that how closing the loop between operational dynamics and investment decisions for microgrids allows us to explicitly incorporate short-term operational volatility and long-term financing and market variations into investment decisions. Here, microgrid cost savings function describing the short-term benefit growth of the micro-grid under short-term (operational) uncertainties is fed into a stochastic long-term investment model. It turns out that cost saving function can be expressed as a regression function on a number of indices defined by design and operational characteristics of microgrids. Two regression models are constructed. Model (I) is a pure linear model and model II includes linear terms and an interaction term. Two sets of experiments (I and II) and results are demonstrated. Experiment (I) focuses on how volatility measures impact investment decisions. In experiment (II) the impact of operational interactions of generation assets in investment decisions is demonstrated.

Closing the loop between short-term operational volatilities and long-term investment risks in microgrids

With the increased environmental concern, the photovoltaic (PV) generation capacity is growing in today's power systems. As the PV penetration rate increases, the intermittency and uncertainty of PV systems will cause frequency regulation issues. When rapid fluctuations take place, the system requires fast responding regulation to recover the frequency within a short period of time. Traditional power plants with slow dynamics are less capable of tracking the fast-changing regulation signal. In this context, a battery energy storage system (BESS) is considered as an effective regulation source to respond immediately to frequency deviations. This paper addresses the sizing issue of an aggregated BESS by a series of system level performance tests with different BESS penetration rates. The evaluation criteria are the control performance standards 1 and 2. Response effectiveness of the BESS with different levels of disturbances is also analyzed, with comparison to that of the traditional power plants. The proposed BESS aggregation controller is also validated using software simulations and a hardware testbed.

Ralph D. Masiello

Papers

Penetration Rate and Effectiveness Studies of Aggregated BESS for Frequency Regulation

Toward Optimal Day-Ahead Scheduling and Operation Control of Microgrids Under Uncertainty

Business Models for Deploying and Operating Energy Storage and Risk Mitigation Aspects

Closing the loop between short-term operational volatilities and long-term investment risks in microgrids

Penetration rate and effectiveness studies of aggregated BESS for frequency regulation