Policy, Markets, and Computation

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Now showing 1 - 5 of 8
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    Potential Impact of Climate Change on the New Zealand Electricity Market
    ( 2020-01-07) Zakeri, Golbon
    The New Zealand electricity sector, dominated by hydroelectric generation, is arguably highly vulnerable to climate change. While the current generation resources seems adequate for maintaining security of supply, in the face of climate change, inflow patterns may change drastically and we need to reassess the adequacy of generation resources. We introduce a novel process for adjusting the historical inflow models to represent various climate scenarios. Our methodology is general and can be applied to any inflow data set coupled with potential climate scenarios, to produce post climate change inflow distributions. We will then use Dynamic Outer Approximation Sampling Algorithm (DOASA) with a distribution of historical inflows, as well as post climate change inflows, to balance the hydro-thermal generation scheduling for New Zealand. This, in turn, provides some insight into the possible effects of climate change on the electricity generation profile of New Zealand. We find that by 2035, at a national scale, the average price and generation is unlikely to be much affected, but the seasonality within these parameters will likely change. Within this near future horizon, we report that a reduction in annual thermal generation may be offset by an average increase in hydroelectric generation along the Waikato river hydro scheme. However, by 2100, we anticipate a degree of stress in the market due to an expected decrease in water availability. In the worst case, we see that thermal generation will increase to ensure the security of supply, and higher prices may be observed.
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    Exploring a Direct Policy Search Framework for Multiobjective Optimization of a Microgrid Energy Management System
    ( 2020-01-07) Gupta, Amandeep ; Liu, Mengwei ; Gold, David ; Reed, Patrick ; Anderson, C. Lindsay
    With an increasing focus on integration of distributed energy resources, it is likely that microgrids will proliferate globally. Microgrid systems will be expected to achieve multiple stakeholder objectives, motivating the study of microgrid operations using a multiobjective framework. A multiobjective perspective has the potential balance the trade-offs implicit to efficient use of available resources. To address this challenge, this paper proposes a simulation based parametric approach for multiobjective optimization for microgrid energy management. The methodology generates a Pareto-approximate set of control policies, to provide a microgrid controller with diverse alternative strategies for utilizing resources to balance competing objectives. The policies also help to illustrate the complex relationships between the objectives, and the consequences of compromises across performance. The methodology is implemented on a test microgrid and the potential benefits are demonstrated with a set of illustrative case studies.
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    An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events
    ( 2020-01-07) Xu, Yijun ; Korkali, Mert ; Mili, Lamine ; Chen, Xiao
    Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy.
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    Leader-Follower Equilibria for Power Markets in Presence of Prosumers
    ( 2020-01-07) Ramyar, Sepehr ; Chen, Yihsu
    Increased penetration of distributed energy resources throughout the power sector has introduced a new entity in electricity markets, namely, prosumers, with the dual nature of concurrent consumption and generation. This paper assesses the market power potential of prosumers (leader) using a Stackelberg model formulated as a mathematical program with equilibrium constraints (MPEC). The MPEC is recast to a mixed integer program where the Wolfe’s duality is used to overcome the bilinear terms in the objective function, and disjunctive constraints are used to replace complementarity conditions. We apply the model to the IEEE 24-Bus Reliability System to illustrate market outcomes. We show that the Stackelberg strategy always yields higher payoff for the prosumers compared to Cournot and perfect competition cases. Moreover, the social surplus resulted from Stackelberg equilibrium, compared to the other strategies, is the highest (lowest) when the prosumer is in short (long) position. Our analysis contributes to understanding the potential outcomes when prosumers are introduced to marketplace in the power sector.
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    A Merchant Transmission Approach for Uniform-Price Electricity Markets
    ( 2020-01-07) Staudt, Philipp ; Oren, Shmuel
    Uniform-price electricity markets as operated in Germany, for instance, rely on a redispatch mechanism after market clearing to ensure the technical feasibility of generation and consumption schedules with regard to grid constraints. This mechanism determines the costs of congestion management and the welfare loss due to the limited transmission capacity. Therefore, the mechanism is suited to incentivize welfare increasing grid expansion. Depending on the distribution of congestion management costs, it can also align stakeholder interests. In this paper, we present an auction mechanism for transmission grid expansion based on the reduction of redispatch expenditures that theoretically leads to a welfare optimal expansion. The mechanism is applied to a case study in Germany. The results show that the developed mechanism supports an improved planning of grid capacity expansion.