Justin M. Foster

Management of electric vehicle charging to mitigate renewable generation intermittency and distribution network congestion

We consider the management of electric vehicle (EV) loads within a market-based Electric Power System Control Area. EV load management achieves cost savings in both (i) EV battery charging and (ii) the provision of additional regulation service required by wind farm expansion. More specifically, we develop a decision support method for an EV Load Aggregator or Energy Service Company (ESCo) that controls the battery charging for a fleet of EVs. A hierarchical decision making methodology is proposed for hedging in the day-ahead market and for playing the real-time market in a manner that yields regulation service revenues and allows for negotiated discounts on the use of distribution network payments. Amongst several potential solutions that are available, we employ a rolling horizon look-ahead stochastic dynamic programming algorithm and report some typical computational experience.

Tractable Transmission Topology Control Using Sensitivity Analysis

The standard economic generation dispatch (ED) minimizes generation costs subject to transmission constraints, where the status of each line, i.e., open or closed, is fixed. Recent research shows that, by optimally dispatching the network topology along with generation resources, significant congestion costs may be avoided. Optimal topology control, i.e., appropriate changes of transmission-line status, for real-sized power networks requires the solution of a computationally intractable mixed-integer linear program; however, it appears that much of the cost savings may be attained by changing the status of just a few lines. This paper proposes tractable transmission topology control policies, which employ sensitivity information readily available from the ED to select candidate lines to change status while maintaining system connectivity. Implementation on the IEEE 118-bus test system found that our best performing policy captured an average of 96% of the potential cost savings. Moreover, the limited computational effort required suggests that these policies could be employed in real-system operations.

Coupling of day ahead and real-time power markets for energy and reserves incorporating local distribution network costs and congestion

Acknowledging that increasing intermittent clean energy generation is likely to impose a bottleneck in the demand for regulation reserves, we investigate potential increases in the supply of regulation service through enhanced participation of loads in electricity markets. Moreover, we focus on future markets where Loads connected at the distribution network participate extensively and in direct competition with centralized generation whole sale market participants. We focus our analysis to distributed PHEV loads and develop a decision support algorithm for optimal bidding to the existing wholesale as well as to prospective retail/distribution market. We argue that generalization to a broad range of load types is reasonably straight forward.

On fast transmission topology control heuristics

The standard optimal power flow (OPF) problem minimizes generation costs over one study period assuming a fixed system topology. The prospect of a smart grid incorporating extensive cyber capabilities enabling significant progress in economic efficiency, reliability and environmental sustainability, ought to transform the OPF problem accordingly. This paper discusses the inclusion of tractable dynamic transmission topology control in the OPF problem based on heuristic control policies derived from individual transmission “line profit” criteria. Simulations on the IEEE 118-bus test system demonstrate the effectiveness of the heuristic policies in reducing production costs. As the algorithms requisite information to identify promising candidate elements for switching is standard output of the OPF solution, the computational effort is up to four orders of magnitude better than dynamic transmission topology control performance reported in the literature.

Plug-In Electric Vehicle and Voltage Support for Distributed Solar: Theory and Application

This paper examines plug-in electric vehicle (PEV) grid integration, which is a specific, yet significant, component of the overall innovation being adopted by the electric power system. We propose a PEV charging policy that considers transmission and distribution integration issues and reacts to market signals across time scales and systems. More specifically, we propose that the PEV should make economic charging decisions every 5 min based on a real-time market energy price signal. On the time scale of seconds, the PEV provides voltage support for the distribution network, which may allow increased penetrations of distributed photovoltaic (PV) solar arrays. Simulation results using Electric Reliability Council of Texas wholesale power market data suggest that this voltage support service may be provided at a low cost to the individual PEV owner (

Energy reserves and clearing in stochastic power markets: The case of plug-in-hybrid electric vehicle battery charging

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Uniform and complex bids for demand response and wind generation scheduling in multi-period linked transmission and distribution markets

50 per year). Therefore, this may prove a more attractive option for supporting distributed PV arrays than distribution network upgrades such as tap-changer-equipped transformers. Finally, we demonstrate the feasibility of our control algorithm through a test system located at the National Renewable Energy Laboratory.

Economic and corrective applications of tractable transmission topology control

Building on our previous work in plug-in-hybrid electric vehicle (PHEV) charging, we study the potential benefits of demand participating in precisely quantified quality of service trades. Given the equivalency of demand and generation modulation in effecting power system cost and stability, we consider demand and generation as market participants with equal rights who engage in a mix of energy and reserve market transactions that clear simultaneously. Using existing market practice in the clearing of energy and reserves, we formulate the optimal bidding strategy of a load aggregator responsible for the battery charging of a fleet of PHEVs as the solution to a stochastic dynamic program (SDP). We show that optimal PHEV energy and regulation service bids lower PHEV charging costs, mitigate local distribution network congestion constraints, and increase system-wide supply of regulation service and thus contribute to the efficient expansion of intermittent clean generation. We propose and implement a tractable approximate SDP solution and report on computational experience using ERCOT and CAISO data.

Load Participation in Electricity Markets: Day-Ahead and Hour-Ahead Market Coupling with Wholesale/Transmission and Retail/Distribution Cost and Congestion Modeling

Power markets serving 70% of US load operate today on period-specific uniform price-quantity bids (UPQBs). However, UPQBs result in a poor representation of utility accruing to many multi period market participants. UPQB can adequately represent utility of consumption only under the restrictive condition that it is additively separable over time. In fact, the additive separability condition is particularly untrue for emerging smart-grid-enabled flexible demands with storage-like characteristics such as EV battery charging and HVAC. We claim that such types of flexible demand exhibit a utility of consumption related to a period-specific state variable - e.g. the battery charge state - whose dynamics are a function of past consumption trajectories. We also claim that wind generation should not be only credited for its energy bids; it should be also charged for the incremental reserves that the Independent System Operator (ISO) must procure to secure system integrity against bids based on volatile wind output forecasts. Appropriate debiting and charging rates equal the market clearing prices resulting from co-optimizing energy and reserve costs. We argue that flexible demand and wind farms that participate in the day-ahead market by submitting UPQBs are motivated to self-dispatch on the basis of energy and reserve clearing price trajectory forecasts. Since actual clearing prices may differ significantly from the forecasts used in the self-dispatch, oscillatory behavior can easily result if actual clearing prices are used as the next forecast. Nevertheless, a smoother price forecast updating process can lead towards Nash Equilibrium. The papers contribution is the proposal of tractable complex bid rules that (i) allow market participants to reveal their true inter temporal utility of consumption and their net revenue from wind generation, and (ii) enable the market operator to compute the actual Nash equilibrium in a single solution of the market clearing algorithm. The tractability and reasonableness of the complex bid rules are demonstrated through numerical examples.

Security-Constrained Transmission Topology Control MILP Formulation Using Sensitivity Factors

Transmission topology control (TC) is currently employed by power system operators for reliability, e.g., in special protection schemes, rather than for economic reasons. The standard economic generation dispatch (ED) minimizes generation costs subject to a fixed transmission network topology. Although the co-optimization of network topology and generation resources results in significant congestion cost avoidance, it requires the solution of a mixed integer program (MIP), which is intractable for even moderate size systems. Our previous work developed near-optimal and yet tractable TC policies that employ sensitivity information readily available from the standard ED solution. This paper reports on advances to tractable TC, including relaxations to the MIP formulation and TC employed in corrective scenarios. Simulation results on the IEEE 118-bus test system found that, besides giving near optimal generation cost savings, our algorithm is very effective in providing corrective switching actions with minimal computational effort.