Optimal Scheduling of Joint Wind-Thermal Systems

This paper is about the joint operation of wind power with thermal power for bidding in day-ahead electricity market. Start-up and variable costs of operation, start-up/shut-down ramp rate limits, and ramp-up limit are modeled for the thermal units. Uncertainty not only due to the electricity market price, but also due to wind power is handled in the context of stochastic mix integer linear programming. The influence of the ratio between the wind power and the thermal power installed capacities on the expected profit is investigated. Comparison between joint and disjoint operations is discussed as a case study

Combined hydro-wind generation bids in a pool-based electricity market

Present regulatory trends are promoting the irect participation of wind energy in electricity markets. The final result of these markets sets the production scheduling for the operation time, including a power commitment from the wind generators. However, wind resources are uncertain, and the final power delivered usually differs from the initial power committed. This imbalance produces an overcost in the system, which must be paid by those who produce it, e.g., wind generators among others. As a result, wind farm revenue decreases, but it could increase by allowing wind farms to submit their bids to the markets together with a hydro generating unit, which may easily modify its production according to the expected imbalance. This paper presents a stochastic optimization technique that maximizes the joint profit of hydro and wind generators in a pool-based electricity market, taking into account the uncertainty of wind power prediction.En prens

Multiple Timescale Dispatch and Scheduling for Stochastic Reliability in Smart Grids with Wind Generation Integration

Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that at each instant the load and generation in the system remain balanced. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in realtime scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve systemwide reliability. More specifically, when the opportunistic users are non-persistent, i.e., a subset of them leave the power market when the real-time price is not acceptable, we obtain closedform solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multitimescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques. We conclude that the proposed multi-scale dispatch and scheduling with real-time pricing can effectively address the volatility and uncertainty of wind generation and energy demand, and has the potential to improve the penetration of renewable energy into smart grids.Comment: Submitted to IEEE Infocom 2011. Contains 10 pages and 4 figures. Replaces the previous arXiv submission (dated Aug-23-2010) with the same titl

Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

Value of thermostatic loads in future low-carbon Great Britain system

This paper quantifies the value of a large population of heterogeneous thermostatically controlled loads (TCLs). The TCL dynamics are regulated by means of an advanced demand side response model (DSRM). It optimally determines the flexible energy/power consumption and simultaneously allocates multiple ancillary services. This model explicitly incorporates the control of dynamics of the TCL recovery pattern after the provision of the selected services. The proposed framework is integrated in a mixed integer linear programming formulation for a multi-stage stochastic unit commitment. The scheduling routine considers inertia-dependent frequency response requirements to deal with the drastic reduction of system inertia under future low-carbon scenarios. Case studies focus on the system operation cost and CO2 emissions reductions for individual TCLs for a) different future network scenarios, b) different frequency requirements, c) changes of TCL parameters (e.g. coefficient of performance, thermal insulation etc.)