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This paper investigates the impacts of installing regulated wind and electricity storage on average price and price volatility in electricity markets. A stochastic bi-level optimization model is developed, which computes the optimal allocation of new wind and battery capacities, by minimizing a weighted sum of the average market price and price volatility. A fixed budget is allocated on wind and battery capacities in the upper-level problem. The operation of strategic/regulated generation, storage, and transmission players is simulated in the lower-level problem using a stochastic (Bayesian) Cournot-based game model. Australia's national electricity market, which is experiencing occasional price peaks, is considered as the case study. Our simulation results quantitatively illustrate that the regulated wind is more efficient than storage in reducing the average price, while the regulated storage more effectively reduces the price volatility. According to our numerical results, the storage-only solution reduces the average price at most by 9.4%, and the wind-only solution reduces the square root of price volatility at most by 39.3%. However, an optimal mixture of wind and storage can reduce the mean price by 17.6% and the square root of price volatility by 48.1%. It also increases the consumer surplus by 1.52%. Moreover, the optimal mixture of wind and storage is a profitable solution unlike the storage-only solution. [ABSTRACT FROM AUTHOR]

The increasing impact of electric vehicles on distribution networks can be alleviated by smart charging—the shifting of electric vehicle load to times when there is available capacity in the network. This work presents a market mechanism for smart charging that optimally allocates available charging capacity in a way that ensures network stability, while at the same time allowing vehicles to express individual preferences regarding their charging rates. Those who want higher rates can receive these, but must pay a higher price. The mechanism takes into account network-specific constraints such as total network load, voltage drop, and phase unbalance. However, since vehicles have differing impacts on these constraints, this leads to unequal access to the available resources (i.e., charging capacity), resulting in an unfair market. An additional constraint can be introduced to level the playing field for all users, but it leads to a reduction in aggregate performance. The mechanism is shown to be efficient and strategy-proof, so users cannot gain an unfair advantage by misrepresenting their preferences. A series of simulations demonstrate the mechanism’s behavior and properties. The results open the door to multi-tiered user plans by demand response aggregators. [ABSTRACT FROM PUBLISHER]

The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer. [ABSTRACT FROM PUBLISHER]