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Trading of wind energy in day-ahead electricity markets entails significant risks. Wind traders are, therefore, interested in mitigating their trading risks in a number of ways. This work suggests addressing this issue in a comprehensive manner by coordinating the trading of wind resources with thermal resources and a pumped-hydro storage system (PHS) and co-optimizing the trading in energy and regulation markets. The trading strategy is modeled as a mixed integer linear stochastic program that takes into account several uncertain parameters, including wind power outputs, prices for energy, balancing, and regulation, and regulation deployments. In this optimization, the trader’s attitude toward risks is controlled by using the metric of conditional value at risk (CVaR). Simulation results show that coordinated and co-optimized trading of the generation resources and PHS significantly improve the total expected profits and mitigates trader’s risks. [ABSTRACT FROM AUTHOR]

While the management of PV generation is the prime application of residential batteries, they can deliver additional services in order to help systems to become cost-competitive. They can level-out the demand and potentially reduce the cost and emissions of the energy system by reducing demand peaks. In this study, community energy storage (CES) is optimised to perform both PV energy time-shift and demand load shifting (using retail tariffs with varying prices blocks) simultaneously. The optimisation method obtains the techno-economic benefits of CES systems as a function of the size of the community ranging from a single home to a 100-home community in two different scenarios for the United Kingdom: the year 2020 and a hypothetical zero emissions target. It is demonstrated that the levelised cost and levelised value of CES systems reach intermediate values to those achieved when both applications are performed independently. For the optimal performance of a battery system being charged from both local PV plants and the grid, our results suggest that the battery should be sized suitable to ensure it can fully discharge during the peak period. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimal energy storage control scheme is investigated in smart grid environments with solar renewable energy. Based on the idea of adaptive dynamic programming (ADP), a self-learning algorithm is constructed to obtain the iterative control law sequence of the battery. Based on the data of the real-time electricity price (electricity rate in brief), the load demand (load in brief), and the solar renewable energy (solar energy in brief), the optimal performance index function, which minimizes the total electricity cost and simultaneously extends the battery's lifetime, is established. A new analysis method of the iterative ADP algorithm is developed to guarantee the convergence of the iterative value function to the optimum under iterative control law sequence for any time index in a period. Numerical results and comparisons are presented to illustrate the effectiveness of the developed algorithm. [ABSTRACT FROM PUBLISHER]

This paper analyzes the impact of an independently-operated large-scale energy storage system on the electricity prices of a fully competitive pool-based electricity market. From a consumer's perspective, the impact of storage operation on energy cost is investigated. The changes in the revenue of generation units and entities caused due to the arbitrage operation of storage facility are also explored. In the current study, we have considered an optimization-based scheme to schedule the operation of a large-scale storage system. Also, a number of ad-hoc strategies to participate in the energy market are examined for the sake of comparison. Modeling the impacts of a large-scale energy storage system can inform planners and operators of the potential effects of storage on the rest of the system and help them to use storage most effectively. These analyses can also inform regulators and policy-makers of the potential societal costs and benefits to energy storage deployments that are not necessarily monetizable by the energy storage owner. [ABSTRACT FROM AUTHOR]

Optimal sizing of residential photovoltaic (PV) generation and energy storage (ES) systems is a timely issue since government polices aggressively promote installing renewable energy sources in many countries, and small-sized PV and ES systems have been recently developed for easy use in residential areas. We in this paper investigate the problem of finding the optimal capacities of PV and ES systems in the context of home load management in smart grids. Unlike existing studies on optimal sizing of PV and ES that have been treated as a part of designing hybrid energy systems or polygeneration systems that are stand-alone or connected to the grid with a fixed energy price, our model explicitly considers the varying electricity price that is a result of individual load management of the customers in the market. The problem we have is formulated by a D-day capacity planning problem, the goal of which is to minimize the overall expense paid by each customer for the planning period. The overall expense is the sum of expenses to buy electricity and to install PV and ES during D days. Since each customer wants to minimize his/her own monetary expense, their objectives look conflicting, and we first regard the problem as a multi-objective optimization problem. Additionally, we secondly formulate the problem as a D-day noncooperative game between customers, which can be solved in a distributed manner and, thus, is better fit to the pricing practice in smart grids. In order to have a converging result of the best-response game, we use the so-called proximal point algorithm. With numerical investigation, we find Pareto-efficient trajectories of the problem, and the converged game-theoretic solution is shown to be mostly worse than the Pareto-efficient solutions. [ABSTRACT FROM AUTHOR]

During power grid contingencies, frequency regulation is a primary concern. Historically, frequency regulation during contingency events has been the sole responsibility of the power utility. We present a practical method of using distributed demand response scheduling to provide frequency regulation during contingency events. This paper discusses the implementation of a control system model for the use of distributed energy storage systems such as battery banks and electric water heaters as a source of ancillary services. We present an algorithm which handles the optimization of demand response scheduling for normal operation and during contingency events. We use dynamic programming as an optimization tool. A price signal is developed using optimal power flow calculations to determine the locational marginal price of electricity, while sensor data for water usage is also collected. Using these inputs to dynamic programming, the optimal control signals are given as output. We assume a market model in which distributed demand response resources are sold as a commodity on the open market and profits from demand response aggregators as brokers of distributed demand response resources can be calculated. In considering control decisions for regulation of transient changes in frequency, we focus on IEEE standard 1547 in order to prevent the safety shut-off of inverter-based generation and further exacerbation of frequency droop. This method is applied to IEEE case 118 as a demonstration of the method in practice. [ABSTRACT FROM AUTHOR]

Price arbitrage involves taking advantage of an electricity price difference, storing electricity during low-prices times, and selling it back to the grid during high-prices periods. This strategy can be exploited by customers in presence of dynamic pricing schemes, such as hourly electricity prices, where the customer electricity cost may vary at any hour of day, and power consumption can be managed in a more flexible and economical manner, taking advantage of the price differential. Instead of modifying their energy consumption, customers can install storage systems to reduce their electricity bill, shifting the energy consumption from on-peak to off-peak hours. This paper develops a detailed storage model linking together technical, economic and electricity market parameters. The proposed operating strategy aims to maximize the profit of the storage owner (electricity customer) under simplifying assumptions, by determining the optimal charge/discharge schedule. The model can be applied to several kinds of storages, although the simulations refer to three kinds of batteries: lead-acid, lithium-ion (Li-ion) and sodium-sulfur (NaS) batteries. Unlike literature reviews, often requiring an estimate of the end-user load profile, the proposed operation strategy is able to properly identify the battery-charging schedule, relying only on the hourly price profile, regardless of the specific facility's consumption, thanks to some simplifying assumptions in the sizing and the operation of the battery. This could be particularly useful when the customer load profile cannot be scheduled with sufficient reliability, because of the uncertainty inherent in load forecasting. The motivation behind this research is that storage devices can help to lower the average electricity prices, increasing flexibility and fostering the integration of renewable sources into the power system. [ABSTRACT FROM AUTHOR]

In the context of day-ahead or real-time electricity prices, the method of economic model predictive control has been shown to provide expenditure reduction in building HVAC systems, specifically if coupled with active thermal energy storage. However, due to the diurnal nature of electricity prices, these expenditure reductions can only be achieved if the economic model predictive control prediction horizon is sufficiently large, typically greater than 12 h. This work develops an alternative controller known as constrained economic linear optimal control. This constrained economic linear optimal control policy is shown to yield economic performance similar to a large-horizon economic model predictive control policy but also possesses a virtual insensitivity to horizon size. Thus, application of the constrained economic linear optimal control policy will require a fraction of the computational effort while yielding nearly identical economic performance. [ABSTRACT FROM PUBLISHER]

Bulk energy storage is generally considered an important contributor for the transition toward a more flexible and sustainable electricity system. Although economically valuable, storage is not fundamentally a "green" technology, leading to reductions in emissions. We model the economic and emissions effects of bulk energy storage providing an energy arbitrage service. We calculate the profits under two scenarios (perfect and imperfect information about future electricity prices), and estimate the effect of bulk storage on net emissions of CO2 SO2, and NOx for 20 eGRID subregions in the United States. We find that net system CO2 emissions resulting from storage operation are nontrivial when compared to the emissions from electricity generation, ranging from 104 to 407 kg/MWh of delivered energy depending on location, storage operation mode, and assumptions regarding carbon intensity. Net NOx emissions range from -0.16 (i.e., producing net savings) to 0.49 kg/MWh, and are generally small when compared to average generation-related emissions. Net SO2 emissions from storage operation range from -0.01 to 1.7 kg/MWh, depending on location and storage operation mode. [ABSTRACT FROM AUTHOR]

We investigate the potential for aggregations of residential thermostatically controlled loads (TCLs), such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control. We present two arbitrage approaches: 1) a benchmark that gives us an optimal policy but requires local computation or real-time communication and 2) an alternative based on a thermal energy storage model, which relies on less computation/communication infrastructure, but is suboptimal. We find that the alternative approach achieves around 60%–80% of the optimal wholesale energy cost savings. We use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in California's intraday energy market. We investigate six sites over four years and find that the savings range from $2–$37 per TCL per year, and depend upon outdoor temperature statistics and price volatility. [ABSTRACT FROM PUBLISHER]

This paper aims to evaluate and improve the usefulness of publicly available electricity market prices for real-time optimal dispatching (RTOD) of a privately owned energy storage system (ESS) in a competitive electricity market. The RTOD algorithm seeks to maximize the revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day-ahead market. The pre-dispatch prices, issued by the Ontario independent electricity system operator, and the corresponding ex-post hourly Ontario energy prices are employed as the forecast and the actual prices. A compressed-air ESS is sized and employed for evaluations due to its lower capital expenditure and its ability to be positively influenced by the availability of waste heat. First, the conventional RTOD algorithm is developed by formulating a mixed integer linear programming problem. It is demonstrated that the forecast inaccuracy of publicly available market prices significantly reduces the ESS revenue. Then, a new adaptive algorithm is proposed and evaluated which adapts the objective function of the optimization problem online based on historical market prices available before real-time. The outcomes reveal that the proposed adaptive RTOD can significantly increase the ESS revenue compared to the conventional algorithm as well as the back-casting method proposed in prior studies. [ABSTRACT FROM PUBLISHER]

Increasingly, large-scale computing systems are consuming more power each passing year. As power consumption is on the rise, concern has been raised over the growing implications on power bills, carbon emissions and power supply limitations for data centers. Computing systems use hardware components that are not power proportional and machines comprising large systems tend to be underutilized, resulting in great wastage of energy. Motivated by this fact, researchers aim to improve the energy efficiency of these systems by increasing resource utilization and exploiting system characteristics in order to achieve power proportionality. However, many challenges burden the task of producing a power-aware, energy-efficient large-scale system that provides the same performance as today's systems. Particularly, storage systems are of great concern since storage consumes a large amount of power in the data center. This paper outlines the main problems and challenges of delivering power-efficient storage solutions, and proposes a taxonomy of power-aware techniques. This work aims to provide a detailed exposition of current trends in power-aware storage systems in a comprehensive and organized manner, outlining and comparing current solutions and challenges. [ABSTRACT FROM AUTHOR]