Abstract

This paper explains why current electricity markets are not fit for purpose and propose a new market design. Electricity markets operating today were designed for the technical and economic conditions of the 1990's. These conditions have changed substantially, especially with increased penetration of intermittent renewables and the growing potential for distributed energy resources and consumer involvement. Today's markets are incompatible with these trends. They do not provide helpful signals for investment, operations, demand response, system optimisation. Nor do they offer an exit from continued subsidies for renewables. To address these problems, we need a new market design. This paper outlines a "two market" model, separating the "as available" market from the "on demand" market, both at the wholesale and retail level. The as available market applies to renewable energy. The on demand market is for flexible plants needed to meet peak demand and to back up intermittent renewables Each market will have its own price.  The two market model emphasizes demand flexibility. It will enable consumers to choose what mix of energy they wish to purchase from the two markets; encourage them to shift their demand to the as available market (and to acquire storage and other load-shifting appliances); provide a market-basis for financing flexible plant; and offer an exit strategy for government subsidies to renewables and flexible plants.

Abstract

Smart charging of electric vehicles (EVs) could potentially facilitate easing network imbalance and grid over-loading issues that are expected to emerge due to the increase in sustainable energy alternatives in the near future. It has been shown that smart charging is technically possible, however, if there are no positive business cases, it is unlikely to succeed. One of the possible business cases is supplying energy in ancillary services markets. This paper investigates this business case for the Dutch ancillary service markets by determining the investment and operational costs of smart charging a pool of EVs and creating a market participation model that combines EV charging data with market data to calculate the potential profit.

Abstract

This paper explains why current electricity markets are not fit for purpose and proposes a new consumer-driven two-market design to support very high penetrations of intermittent renewable energy. The structure encourages demand flexibility; enables consumers to choose what mix of energy they wish to purchase from the two markets (as available renewable energy or on demand flexible energy); encourages them to shift their demand to the as available market (and to acquire storage and other load-shifting appliances); provides a market-basis for financing flexible plant; and offers an exit strategy for government subsidies to renewables and flexible plants

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Abstract

Electric Vehicles (EVs) are emerging among the Distributed Energy Resources (DERs) as a promising flexibility source. The small-scale of DERs still constitutes a barrier to their direct participation in electricity markets. Aggregators can exploit EV smart charging to lower the energy procurement cost in the day-ahead (DA) scheduling phase. This cost minimization should consider the uncertainty stemming from the availability of EVs, the DA and the imbalance market prices. To catch all these uncertainties, a novel approach combining robust optimization and stochastic programming is proposed to define the DA charging schedule for an EV fleet, considering the DA market prices and the possible imbalance realizations in real-time. This method is compared with a two-stage stochastic (TSS) programming approach and with the EV uncontrolled charging. Two weeks of data from the DA spot market in the Netherlands have been used for comparing the methods with different fleet sizes: 100, 200 and 400 EVs. The results show that the hybrid robust-stochastic method, while keeping approximately the same average DA energy cost, can estimate better than the TSS method the actual daily energy cost with reduced computational time.