The analysis is based on a newly developed capacity expan sion model

The analysis is based on a newly developed capacity

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The analysis is based on a newly developed capacity expan- sion model incorporating both low carbon policy constraints and a full set of chronological flexibility constraints. The proposed model determines in a single optimization both the investment capacities for every type of generator in every region, and the commitment and dispatch decisions for the 8760-hour operation. The investment decisions are linked directly to the dispatch de- cisions, and the hourly power balance optimizes commitment and dispatch for thermal units applying the full set of flexibility constraints (minimal on/off time, ramping, minimal output level, reserve constraints), with respect to interprovincial transmission limitations, variations of renewables (from hourly to seasonal) and power demand. The optimization model also incorporates low carbon policy constraints, such as RPS, renewable produc- tion tax credits, carbon taxes, and carbon cap-and-trade. The decision variables and flexibility constraints related to hourly system operation are formulated linearly in this paper adopting unit clustering techniques. This modeling formulation accelerates markedly the computational speed, which makes the multi-region, 8760-hour simulation computationally feasible in the single optimization model. The improved computational speed is important also for incorporation of full-year energy binding low carbon policies such as RPS and carbon cap-and- trade systems. A novel linear model is developed for the optimized investment and operation of storage technologies. In making investment decisions, we take into account diverse storage tech- nologies, differences for energy/power related costs, variations of geographical areas and life-time watt-hours. In simulating the operation of storage systems, the model considers the contributions of energy storage for both power balance and provision of reserve. We also differentiate efficiency or loss for the charging, discharging and self-discharge. Particular attention is directed to the relationship between the storage modeling and renewable policies. Results indicate that the generation mix for thermal units will change drastically when introducing flexibility constraints at higher renewable levels. With the increase of renewable pen- etration, the geographical allocation of renewable investments will be distributed more evenly across regions and the opti- mal investment of thermal power will shift from high efficiency large coal units, to more flexible mid-size coal fired units and gas-fired units. Solar power will be deployed only after the re- newable penetration reaches 55%. The carbon reduction cost at 35% of RPS is acceptable compared to the social cost of carbon, while a further increase of renewable penetration is expensive and sensitive to the price of energy storage.
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