Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from, our method is data-driven, and captures renewable energy production patterns in both temporal and spatial dimensions for a large number of correlated resources. For validation, we use wind and solar times-series data from NREL integration data sets. We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors. We also illustrate how to generate scenarios based on different conditions of interest by using labeled data during training. For example, scenarios can be conditioned on weather events~(e.g. high wind day, intense ramp events or large forecasts errors) or time of the year~(e,g. solar generation for a day in July). Because of the feedforward nature of the neural networks, scenarios can be generated extremely efficiently without sophisticated sampling techniques

Energy storage can alleviate the problems that the uncertainty and variability associated with renewable energy sources such as wind and solar create in power systems. Besides applications such as frequency control, temporal arbitrage or the provision of reserve, where the location of storage is not particularly relevant, distributed storage could also be used to alleviate congestion in the transmission network. In such cases, the siting and sizing of this distributed storage is of crucial importance to its cost-effectiveness. This paper describes a three-stage planning procedure to identify the optimal locations and parameters of distributed storage units. In the first stage, the optimal storage locations and parameters are determined for each day of the year individually. In the second stage, a number of storage units is available at the locations that were identified as being optimal in the first stage, and their optimal energy and power ratings are determined. Finally, in the third stage, with both the locations and ratings fixed, the optimal operation of the storage units is simulated to quantify the benefits that they would provide by reducing congestion. The quality of the final solution is assessed by comparing it with the solution obtained at the first stage without constraints on storage sites or size. The approach is numerically tested on the IEEE RTS 96.

Transmission expansion and energy storage increase the flexibility of power systems and, hence, their ability to deal with uncertainty. Transmission lines have a longer lifetime and a more predictable performance than energy storage, but they require a very large initial investment. While battery energy storage systems (BESS) can be built faster and their capacity can be increased gradually, their useful life is shorter because their energy capacity degrades with time and each charge and discharge cycle. Additional factors, such as the expected profiles of load and renewable generation significantly affect planning decisions. This paper proposes a stochastic, multistage, coplanning model of transmission expansion, and BESS that considers both the delays in transmission expansion and the degradation in storage capacity under different renewable generation and load increase scenarios. The proposed model is tested using a modified version of the IEEE-RTS. Sensitivity analyses are performed to assess how factors such as the planning method, the storage chemistry characteristics, the current transmission capacity, and the uncertainty on future renewable generation and load profiles affect the investment decisions.

The large-scale integration of a grid-scale energy storage and the increasing penetration of renewable resources motivate the development of techniques for determining the optimal ratings and locations of storage devices. This paper proposes a method for identifying the sites where energy storage systems should be located to perform spatio-temporal energy arbitrage most effectively and the optimal size of these systems. This method takes a centralized perspective where the objective is to minimize the sum of the expected operating cost and the investment cost of energy storage. It has been tested on a realistic 240-bus 448-line model of the Western Electricity Coordinating Council (WECC) interconnection. The influence on the results of the following parameters is analyzed: Maximum number of storage locations, maximum size of storage systems, capital cost of deploying storage, value assigned to spillage of renewable energy, marginal cost of conventional generation, and renewable generation capacity. These numerical results are used to characterize the benefits that energy storage can provide in prospective large-scale power systems with renewable generation.

As the cost of battery energy storage continues to decline, we are likely to see the emergence of merchant energy storage operators. These entities will seek to maximize their operating profits through strategic bidding in the day-ahead electricity market. One important parameter in any storage bidding strategy is the state-of-charge at the end of the trading day. Because this final state-of-charge is the initial state-of-charge for the next trading day, it has a strong impact on the profitability of storage for this next day. This paper proposes a look-ahead technique to optimize a merchant energy storage operator's bidding strategy considering both the day-ahead and the following day. Taking into account the discounted profit opportunities that could be achieved during the following day allows us to optimize the state-of-charge at the end of the first day. We formulate this problem as a bilevel optimization. The lower-level problem clears a ramp-constrained multiperiod market and passes the results to the upper-level problem that optimizes the storage bids. Linearization techniques and Karush-Kuhn-Tucker conditions are used to transform the original problem into an equivalent single-level mixed-integer linear program. Numerical results obtained with the IEEE Reliability Test System demonstrate the benefits of the proposed look-ahead bidding strategy and the importance of considering ramping and network constraints.

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.