A Probabilistic Framework for Reserve Scheduling and ${\rm N}-1$ Security Assessment of Systems With High Wind Power Penetration

Abstract:In recent deregulated power systems, demand response (DR) has become one of the most cost-effective and efficient solutions for smoothing the load profile when the system is under stress. By participating in DR programs, customers are able to change their energy consumption habits in response to energy price changes and get incentives in return. In this paper, we study the effect of various time-based rate (TBR) programs on the stochastic day-ahead energy and reserve scheduling in residential islanded microgrids (MGs). An effective approach is presented to schedule both energy and reserve in presence of renewable energy resources (RESs) and electric vehicles (EVs). An economic model of responsive load is also proposed on the basis of elasticity factor to model the behavior of customers participating in various DR programs. A two-stage stochastic programming model is developed accordingly to minimize the expected cost of MG under different TBR programs. To verify the effectiveness and applicability of the proposed approach, a number of simulations are performed under different scenarios using real data; and the impact of TBR-DR actions on energy and reserve scheduling are studied and compared subsequently.

show abstract

“…∀l, ∀t (19) • Real power generation constraints; The real power generated by DG units are constrained by (20) and (21) [21].…”

Section: Constraintsmentioning

confidence: 99%

Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

et al. 2017

View full text Add to dashboard Cite

show abstract

“…By showing how (14) is related to the traditional N-1 criterion, we provide a physical interpretation of the risk limit and give some intuition how R i can be chosen. Assuming that P i is a scalar value given as an input to the optimization, (14) can be reformulated as an upper bound on the severity…”

Section: ) Relation To N-1 Constraintsmentioning

confidence: 99%

“…Further details on how to compute D is given in [9], [14]. In the second step, we use the probabilistically computed set D to solve a robust problem for all uncertainty realizations within this set.…”

Section: A Chance Constraint Reformulationmentioning

confidence: 99%

Risk-constrained optimal power flow with probabilistic guarantees

Roald¹,

Vrakopoulou²,

Oldewurtel³

et al. 2014

2014 Power Systems Computation Conference

Self Cite

View full text Add to dashboard Cite

Abstract-Higher penetration of renewable energy and market liberalization increase both the need for transmission capacity and the uncertainty in power system operation. New methods for power system operational planning are needed to allow for efficient use of the grid, while maintaining security and robustness against disturbances. In this paper, we propose a risk model for risk related to outages, accounting for available remedial measures and the impact of cascading events. The new risk model is used to formulate risk-based constraints for the postcontingency line flows, which are included in an optimal power flow (OPF) formulation. Forecast uncertainty is accounted for by formulating the relevant constraints as a chance constraint, and the problem is solved using a sampling based technique. In a case study for the IEEE 30 bus system, we demonstrate how the proposed risk-based, probabilistic OPF allows us to control the risk level, even in presence of uncertainty. We investigate the trade-off between generation cost and risk level in the system, and show how accounting for uncertainty leads to a more expensive, but more secure dispatch.Keywords-risk-based optimal power flow, chance constrained optimal power flow, security, wind power integration

show abstract

“…Notice that there is no theoretical guarantee on algorithms in [13]. The randomization technique is also applied for robust convex optimization [17], [18] and there is an application to a robust DC-OPF [19]. However, a constraint of AC-OPF for each renewable power generation is nonconvex.…”

Section: Introductionmentioning

confidence: 99%

A Randomized Algorithm for Chance Constrained Optimal Power Flow with Renewables

Wada

Morita

Asai

et al. 2017

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

: A chance constrained AC optimal power flow is to find the optimal economic operation plan whose probability satisfying AC power flow equations and various inequality constraints on operating limits of the power system is greater than a specified probability level. Even if a constraint condition for each uncertain power supply value is convex with respect to decision variables, the chance constrained problem is not convex in general. Thus, it is difficult to solve the problem within reasonable computational time. Employment of randomization techniques for this issue is proposed in this paper. A main advantage of the framework leads to a solution with a theoretical guarantee. Its sample complexities are of polynomial order for parameters of a given accuracy. The efficiency of this algorithm is demonstrated by applying it to the Japanese power system models.

show abstract

A Probabilistic Framework for Reserve Scheduling and ${\rm N}-1$ Security Assessment of Systems With High Wind Power Penetration

Cited by 234 publications

References 21 publications

Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

Study of the Effect of Time-Based Rate Demand Response Programs on Stochastic Day-Ahead Energy and Reserve Scheduling in Islanded Residential Microgrids

Risk-constrained optimal power flow with probabilistic guarantees

A Randomized Algorithm for Chance Constrained Optimal Power Flow with Renewables

Contact Info

Product

Resources

About