Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms, e.g., pump-storage hydro, large-scale, or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping, and price arbitrage capabilities. In this paper, we propose a stochastic bilevel optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic regulated generation, storage, and transmission players is modeled in the lower level problem using an extended stochastic (Bayesian) Cournot-based game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, and a 30-bus IEEE system are considered as the case studies. Our numerical results indicate that 50 price volatility reduction in the SA electricity market can be achieved by installing either 430-MWh regulated storage or 530-MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.
The problem of optimal storage operation or storage allocation for facilitating the integration of intermittent renewable energy generators in electricity networks has been studied in @cite_26 @cite_3 @cite_33 @cite_22 @cite_27 @cite_20 @cite_6 @cite_30 , with total cost minimization objective functions, and in @cite_12 @cite_24 @cite_10 @cite_18 @cite_1 , with profit maximization goals. However, the price volatility management problem using optimal storage allocation has not been investigated in the literature.
@cite_30 @cite_18 @cite_26 @cite_22 @cite_33 @cite_1 @cite_3 @cite_6 @cite_24 @cite_27 @cite_10 @cite_12 @cite_20
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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. 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. The high variability of renewable energy is a major obstacle toward its increased penetration. Energy storage can help reduce the power imbalance due to the mismatch between the available renewable power and the load. How much can storage reduce this power imbalance? How much storage is needed to achieve this reduction? This paper presents a simple analytic model that leads to some answers to these questions. Considering the multitimescale grid operation, we formulate the power imbalance problem for each timescale as an infinite horizon stochastic control problem and show that a greedy policy minimizes the average magnitude of the residual power imbalance. Observing from the wind power data that in shorter timescales the power imbalance can be modeled as an iid zero-mean Laplace distributed process, we obtain closed form expressions for the minimum cost and the stationary distribution of the stored power. We show that most of the reduction in the power imbalance can be achieved with relatively small storage capacity. In longer timescales, the correlation in the power imbalance cannot be ignored. As such, we relax the iid assumption to a weakly dependent stationary process and quantify the limit on the minimum cost for arbitrarily large storage capacity. In this paper, we study power allocation for secure communication in a multiuser multiple-input single-output (MIS-O) downlink system with simultaneous wireless information and power transfer. The receivers are able to harvest energy from the radio frequency when they are idle. We propose a multi-objective optimization problem for power allocation algorithm design which incorporates two conflicting system objectives: total transmit power minimization and energy harvesting efficiency maximization. The proposed problem formulation takes into account a quality of service (QoS) requirement for the system secrecy capacity. Our designs advocate the dual use of artificial noise in providing secure communication and facilitating efficient energy harvesting. The multi-objective optimization problem is non-convex and is solved by a semidefinite programming (SDP) relaxation approach which results in an approximate of solution. A sufficient condition for the global optimal solution is revealed and the accuracy of the approximation is examined. To strike a balance between computational complexity and system performance, we propose two suboptimal power allocation schemes. Numerical results not only demonstrate the excellent performance of the proposed suboptimal schemes compared to baseline schemes, but also unveil an interesting trade-off between energy harvesting efficiency and total transmit power.