The dominant transitional path towards a low carbon electricity industry for systems which have been heavily dependent upon coal is through its replacement by large scale wind farms and the widespread emergence of distributed solar. In this pathway, maintaining resource adequacy in the context of increased intermittency in generation has become a major concern. This paper examines this requirement to maintain resource adequacy and compare the costs and carbon impacts for new gas turbines or biomass conversions to achieve this in an expedient transitional way. This is formulated as a policy optimization in which the imperative is to replace existing coal with a renewable alternative (in this case study, wind) and to maintain the system security at the existing level, and thereby find the optimal subsidies, either as energy credits ("green certificates" or "contracts-for-differences") or capital benefits ("capacity payments" or tax allowances). In a model of the GB system, the results show that that biomass-conversion outperforms investment in peaking gas turbines to deal with the transitional economic externality of extra reserve costs. In particular, the results suggest benefits of 10% lower costs of subsidies, 70% lower implied costs of carbon, and a reduction of 18% in wholesale power prices.
The recent incorporation of Electric Vehicles (EVs) in the worldwide delivery vehicle fleet has increased the importance of their operation in road networks. In particular, the growth of electronic commerce, while promoting the use of non-polluting energy sources, will make EVs play an important role in cities. Thus, an accurate characterization of the technical aspects of EVs and parcel delivery requirements in delivery route operational planning models is required. To that end, this paper presents an enhanced operational planning model for the route and charging of an EV fleet considering technical and economic real-world constraints, such as battery degradation, acceleration-and speed-dependent power consumption, tolls and penalty for delivery delay and non-fulfillment. Moreover, unlike some previous works, the delivery allocation and the number of EVs used are not predetermined, being rather outcomes of the optimization process. Additionally, the Proposed Approach (PA) allows EVs to pass through intersections more than once. The proposed model is formulated in terms of a series of intersection-and path-related decision variables, characterizing the State of Charge (SOC) of batteries and the navigation time. The resulting optimization problem is cast as an instance of Mixed-Integer Linear Programming (MILP). The model is implemented in the mathematical programming language GAMS and solved using the commercial solvers CPLEX and ODH-CPLEX. The model is tested on two intersection maps, including different types of roads, charging rates and delivery points. Results show the effectiveness of the PA over previously reported approaches in terms of the cost, energy and time associated with the resulting operating strategies. INDEX TERMS Battery degradation, delivery allocation, electric vehicle route planning, penalty costs, acceleration-and speed-dependent power consumption. NOMENCLATURE SETS AND INDICES
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