4.7 Article

Secure Planning and Operations of Systems With Stochastic Sources, Energy Storage, and Active Demand

Journal

IEEE TRANSACTIONS ON SMART GRID
Volume 4, Issue 4, Pages 2220-2229

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2013.2281001

Keywords

Energy storage; optimization; power engineering computing; power generation dispatch; power system analysis computing; power system economics; power system planning; power system reliability; power system simulation; renewable energy sources; smart grids; uncertainty; wind energy

Funding

  1. Consortium for Electric Reliability Technology Solutions
  2. Office of Electricity Delivery and Energy Reliability, Transmission Reliability Program of the U.S. Department of Energy under the National Energy Technology Laboratory [DE-FC26-09NT43321]

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This work presents a stochastic optimization framework for operations and planning of an electricity network as managed by an Independent System Operator. The objective is to maximize the total expected net benefits over the planning horizon, incorporating the costs and benefits of electricity consumption, generation, ancillary services, load-shedding, storage and load-shifting. The overall framework could be characterized as a secure, stochastic, combined unit commitment and AC optimal power flow problem, solving for an optimal state-dependent schedule over a pre-specified time horizon. Uncertainty is modeled to expose the scenarios that are critical for maintaining system security, while properly representing the stochastic cost. The optimal amount of locational reserves needed to cover a credible set of contingencies in each time period is determined, as well as load-following reserves required for ramping between time periods. The models for centrally-dispatched storage and time-flexible demands allow for optimal tradeoffs between arbitraging across time, mitigating uncertainty and covering contingencies. This paper details the proposed problem formulation and outlines potential approaches to solving it. An implementation based on a DC power flow model solves systems of modest size and can be used to demonstrate the value of the proposed stochastic framework.

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