Anthony Papavasiliou

Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework

We present a two-stage stochastic programming model for committing reserves in systems with large amounts of wind power. We describe wind power generation in terms of a representative set of appropriately weighted scenarios, and we present a dual decomposition algorithm for solving the resulting stochastic program. We test our scenario generation methodology on a model of California consisting of 122 generators, and we show that the stochastic programming unit commitment policy outperforms common reserve rules.

Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network

In this paper we present a unit commitment model for studying the impact of large-scale wind integration in power systems with transmission constraints and system component failures. The model is formulated as a two-stage stochastic program with uncertain wind production in various locations of the network as well as generator and transmission line failures. We present a scenario selection algorithm for selecting and weighing wind power production scenarios and composite element failures, and we provide a parallel dual decomposition algorithm for solving the resulting mixed-integer program. We validate the proposed scenario selection algorithm by demonstrating that it outperforms alternative reserve commitment approaches in a 225 bus model of California with 130 generators and 375 transmission lines. We use our model to quantify day-ahead generator capacity commitment, operating cost impacts, and renewable energy utilization levels for various degrees of wind power integration. We then demonstrate that failing to account for transmission constraints and contingencies can result in significant errors in assessing the economic impacts of renewable energy integration.

Supplying renewable energy to deferrable loads: Algorithms and economic analysis

In this paper we propose a direct coupling of renewable generation with deferrable demand in order to mitigate the unpredictable and non-controllable fluctuation of renewable power supply. We cast our problem in the form of a stochastic dynamic program and we characterize the value function of the problem in order to develop efficient solution methods. We develop and compare two algorithms for optimally supplying renewable power to time-flexible electricity loads in the presence of a spot market, backward dynamic programming and approximate dynamic programming. We describe how our proposition compares to price responsive demand in terms capacity gains and energy market revenues for renewable generators, and we determine the optimal capacity of deferrable demand which can be reliably coupled to renewable generation.

Large-Scale Integration of Deferrable Demand and Renewable Energy Sources

We present a stochastic unit commitment model for assessing the impacts of the large-scale integration of renewable energy sources and deferrable demand in power systems in terms of reserve requirements. We analyze three demand response paradigms for assessing the benefits of demand flexibility: the centralized co-optimization of generation and demand by the system operator, demand bids and the coupling of renewable resources with deferrable loads. We motivate coupling as an alternative for overcoming the drawbacks of the two alternative demand response options and we present a dynamic programming algorithm for coordinating deferrable demand with renewable supply. We present simulation results for a model of the Western Electricity Coordinating Council.

Applying High Performance Computing to Transmission-Constrained Stochastic Unit Commitment for Renewable Energy Integration

We present a parallel implementation of Lagrangian relaxation for solving stochastic unit commitment subject to uncertainty in renewable power supply and generator and transmission line failures. We describe a scenario selection algorithm inspired by importance sampling in order to formulate the stochastic unit commitment problem and validate its performance by comparing it to a stochastic formulation with a very large number of scenarios, that we are able to solve through parallelization. We examine the impact of narrowing the duality gap on the performance of stochastic unit commitment and compare it to the impact of increasing the number of scenarios in the model. We report results on the running time of the model and discuss the applicability of the method in an operational setting.

Market-based control mechanisms for electric power demand response

We propose a settlement mechanism for optimally scheduling real time electricity consumption which is suitable for an automated demand response control system. Our proposed settlement mechanism, supply function bidding, is interpreted as a Newton algorithm for optimization problems with decomposable structure, and it is shown to satisfy the second fundamental theorem of welfare economics for the case of affine supply function bids. We simulate the behavior of our proposed control mechanism for the case of demand response via home temperature control, and we demonstrate how a suboptimal control policy can have adverse impacts both in terms of system performance and also in terms of economic incentives.

Coupling Wind Generators with Deferrable Loads

We explore the possibility of directly coupling deferrable loads with wind generators in order to mitigate the variability and randomness of wind power generation. Loads engage in a contractual agreement of deferring their demand for power by a fixed amount of time and wind generators optimally allocate available wind power with the objective of minimizing the cost of unscheduled and variable supply. We simulate the performance of the proposed coupling in a market environment and we demonstrate its compatibility with existing technology, grid operations and economic incentives. The results indicate that the combination of existing deregulated power markets and demand side flexibility could support large scale integration of wind power without significant impacts on grid operations and without the requirement for prohibitive investments in backup generation.

Self-Commitment of Combined Cycle Units Under Electricity Price Uncertainty

Summary form only given. Day-ahead energy market clearing relies on a deterministic equivalent model with a limited time horizon, which may lead to inefficient scheduling of generating units from the point of view of generators. For this reason, generators may wish to assume the risk of self-committing their units with the hope of securing greater profits. This phenomenon may reduce the room for economic signals in the day-ahead market. In this paper we investigate the influence of risk aversion and price volatility on the decision of generators to self-commit units. We present a stochastic programming model for self-committing combined cycle units under price uncertainty with a conditional value at risk criterion. We use Benders decomposition to solve the problem and present results on a case study to draw conclusions.

A stochastic unit commitment model for integrating renewable supply and demand response

We present a stochastic unit commitment model for assessing the reserve requirements resulting from the large-scale integration of renewable energy sources and deferrable demand in power systems. We present three alternative demand response paradigms for assessing the benefits of demand flexibility in absorbing the uncertainty and variability associated with renewable supply: centralized co-optimization of generation and demand by the system operator, demand bids and coupling renewable resources with deferrable loads. We present simulation results for a model of the Western Interconnection.

The Impacts of Transmission Topology Control on the European Electricity Network

The European Union is targeting a 20% share of energy from renewable resources by 2020, and this increase is in turn expected to lead to operational challenges that will require various congestion management actions by system operators. In this paper, we deal with topology control of the transmission network as a congestion management resource and evaluate the impacts of topology control on the European electricity network. To do this, we co-optimize unit commitment and transmission switching over 24 h, and we use a decomposition scheme to tackle the resulting large-scale problem. Our analysis is conducted on different scenarios of load and renewable power generation. We find that topology control results in significant cost savings within Europe which tend to be inversely related to net load. The robustness of our results is supported by an extensive sensitivity analysis.