1,399 research outputs found
Risk Limiting Dispatch with Ramping Constraints
Reliable operation in power systems is becoming more difficult as the
penetration of random renewable resources increases. In particular, operators
face the risk of not scheduling enough traditional generators in the times when
renewable energies becomes lower than expected. In this paper we study the
optimal trade-off between system and risk, and the cost of scheduling reserve
generators. We explicitly model the ramping constraints on the generators. We
model the problem as a multi-period stochastic control problem, and we show the
structure of the optimal dispatch. We then show how to efficiently compute the
dispatch using two methods: i) solving a surrogate chance constrained program,
ii) a MPC-type look ahead controller. Using real world data, we show the chance
constrained dispatch outperforms the MPC controller and is also robust to
changes in the probability distribution of the renewables.Comment: Shorter version submitted to smartgrid comm 201
Distributed Stochastic Market Clearing with High-Penetration Wind Power
Integrating renewable energy into the modern power grid requires
risk-cognizant dispatch of resources to account for the stochastic availability
of renewables. Toward this goal, day-ahead stochastic market clearing with
high-penetration wind energy is pursued in this paper based on the DC optimal
power flow (OPF). The objective is to minimize the social cost which consists
of conventional generation costs, end-user disutility, as well as a risk
measure of the system re-dispatching cost. Capitalizing on the conditional
value-at-risk (CVaR), the novel model is able to mitigate the potentially high
risk of the recourse actions to compensate wind forecast errors. The resulting
convex optimization task is tackled via a distribution-free sample average
based approximation to bypass the prohibitively complex high-dimensional
integration. Furthermore, to cope with possibly large-scale dispatchable loads,
a fast distributed solver is developed with guaranteed convergence using the
alternating direction method of multipliers (ADMM). Numerical results tested on
a modified benchmark system are reported to corroborate the merits of the novel
framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9
figure
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
Chance-constrained optimization (CCO) has been widely used for uncertainty
management in power system operation. With the prevalence of wind energy, it
becomes possible to consider the wind curtailment as a dispatch variable in
CCO. However, the wind curtailment will cause impulse for the uncertainty
distribution, yielding challenges for the chance constraints modeling. To deal
with that, a data-driven framework is developed. By modeling the wind
curtailment as a cap enforced on the wind power output, the proposed framework
constructs a Gaussian process (GP) surrogate to describe the relationship
between wind curtailment and the chance constraints. This allows us to
reformulate the CCO with wind curtailment as a mixed-integer second-order cone
programming (MI-SOCP) problem. An error correction strategy is developed by
solving a convex linear programming (LP) to improve the modeling accuracy. Case
studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that
the proposed method is capable of accurately accounting the influence of wind
curtailment dispatch in CCO
- …