In an isolated power grid or a micro-grid with a small carbon footprint, the penetration of renewable energy is usually high. In such power grids, energy storage is important to guarantee an uninterrupted and stable power supply for end users. Different types of energy storage have different characteristics, including their round-trip efficiency, power and energy rating, energy loss over time, and investment and maintenance costs. In addition, the load characteristics and availability of different types of renewable energy sources vary in different geographic regions and at different times of year. Therefore joint capacity optimization for multiple types of energy storage and generation is important when designing this type of power systems. In this paper, we formulate a cost minimization problem for storage and generation planning, considering both the initial investment cost and operational maintenance cost, and propose a distributed optimization framework to overcome the difficulty brought about by the large size of the optimization problem. The results will help in making decisions on energy storage and generation capacity planning in future decentralized power grids with high renewable penetrations.
There have been several works on optimization with energy storages and renewable generation, and we briefly review some of them here. In @cite_2 , the authors investigated the combined optimization of a wind farm and a pumped storage facility from the perspective of a generation company, using a two-step stochastic optimization approach. The optimization produces optimal bids for the day-ahead spot market, and optimal operation strategies of the facilities. The optimal planning of generation and energy storage capacity was not considered. Zhou @cite_4 proposed a composite energy storage system that contains both high energy density storage and high power density storage. The proposed power converter configuration enables actively distributing demands among different energy storages. Brown provided an economical analysis of the benefits of having pumped storage in a small island system with abundant renewable energy, and proposed to find the optimal pumped storage capacity through linear programming. In @cite_5 , the authors considered optimizing the rating of energy storage in a wind-diesel isolated grid, and demonstrated that high wind penetration potentially results in significant cost savings in terms of fuel and operating costs.
@cite_5 @cite_4 @cite_2
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This paper focuses on a scenario where a group of independently-operated investor-owned storage units seek to offer both energy and reserve in the day-ahead as well as the hour-ahead markets. We are particularly interested in the case when a significant portion of the power generated in the grid is from wind and other intermittent renewable energy sources. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units. Our design takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. We show that the formulated stochastic program can be converted into a convex optimization problem and therefore it can be solved efficiently. Our simulation results show that our design can assure profitability of the private investment on storage units. In particular, our design results in much higher profit compared to a similar but deterministic design that simply uses the expected values of the price parameters. One of the main characteristics of wind power is the inherent variability and unpredictability of the generation source, even in the short-term. To cope with this drawback, hydro pumped-storage units have been proposed in the literature as a good complement to wind generation due to their ability to manage positive and negative energy imbalances over time. This paper investigates the combined optimization of a wind farm and a pumped-storage facility from the point of view of a generation company in a market environment. The optimization model is formulated as a two-stage stochastic programming problem with two random parameters: market prices and wind generation. The optimal bids for the day-ahead spot market are the ldquohere and nowrdquo decisions while the optimal operation of the facilities are the recourse variables. A joint configuration is modeled and compared with an uncoordinated operation. A realistic example case is presented where the developed models are tested with satisfactory results. In an isolated power grid or a micro-grid with a small carbon footprint, the penetration of renewable energy is usually high. In such power grids, energy storage is important to guarantee an uninterrupted and stable power supply for end users. Different types of energy storage have different characteristics, including their round-trip efficiency, power and energy rating, self-discharge, and investment and maintenance costs. In addition, the load characteristics and availability of different types of renewable energy sources vary in different geographic regions and at different times of year. Therefore joint capacity optimization for multiple types of energy storage and generation is important when designing this type of power systems. In this paper, we formulate a cost minimization problem for storage and generation planning, considering both the initial investment cost and operational maintenance cost, and propose a distributed optimization framework to overcome the difficulty brought about by the large size of the optimization problem. The results will help in making decisions on energy storage and generation capacity planning in future decentralized power grids with high renewable penetrations. In this paper, a new optimization framework is proposed to coordinate the operation of large, price-maker, and geographically dispersed energy storage battery systems in a nodal transmission-constrained energy market. The energy storage units are assumed to be investor-owned and independently-operated, seeking to maximize their total profit. Various design factors are taken into consideration such as the location, size, efficiency, and charge and discharge rates of the energy storage units as well as the joint impact of the energy storage operations on the locational marginal prices. While the formulated optimization problem is originally nonlinear and hard to solve, nonlinearities are tackled both in the objective function and in the constraints and the problem is transformed into a tractable mixed-integer linear program, for which the global optimal solutions are found for the charge and discharge schedules of each energy storage unit. Both deterministic and stochastic design scenarios are addressed. Various case studies are presented. It is observed that transmission line congestion is often, but not always, desirable for the coordinated storage systems. Locational diversity, practice of arbitrage, robust design, self-scheduling versus economic bidding, and the overall power system performance are also investigated.