Multi-timescale robust dispatching for coordinated automatic generation control and energy storage

The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control(AGC).Thus,there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units,improve the adjustment potential,and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis,the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

Multi-stage power source and grid coordination planning method considering grid uniformity

Given the increasing uncertainties in power supply and load,this paper proposes the concept of power source and grid coordination uniformity planning.In this approach,the standard deviation of the transmission line load rate is considered as the uniformity evaluation index for power source and grid planning.A multi-stage and multi-objective optimization model of the power source and grid expansion planning is established to minimize the comprehensive cost of the entire planning cycle.In this study,the improved particle swarm optimization algorithm and genetic algorithm are combined to solve the model,thus improving the efficiency and accuracy of the solution.The analysis of a simple IEEE Garver’s 6-node system shows that the model and solution method are effective and feasible.Moreover,they are suitable for the coordinated planning of the power source and grid under a diversified nature of power supply and load.

Integrated generation–consumption dispatch based on compensation mechanism considering demand response behavior

Demand response(DR) is important to account for behaviors of the demand side to yield an optimal dispatch result. However, it is difficult for energy suppliers to collect customers' private information unless there is an incentive mechanism for customers to do so. Therefore,this paper proposes a new integrated generation–consumption dispatch based on compensation mechanism considering DR behavior. Firstly, in light of the dayahead load forecast data, we deduce the utility function model of different customers. By subtracting generating units' operation cost from consumers' total utility, the dispatch model has a decentralized demand participant structure based on this utility function. The utility function is used to describe consumers' preferences and energy consumption behaviors. Secondly, an effective compensation mechanism is designed to ensure customers to select the level of compensation appropriate to their willingness to curtail load. Finally, a new dispatch model is proposed that incorporates the DR compensation mechanism into the generation–consumption dispatch model. The new model can improve the interaction of generation and consumption,and benefit both the energy supplier and its customers. The proposed model is piecewise linearized and solved by a mixed-integer linear programming method. It is tested on a six-bus system and the IEEE 118-bus system. Simulation results show that the proposed model can realize both maximum social welfare and Pareto optimal results.

Multi-dimensional scenario forecast for generation of multiple wind farms

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

Frequency aware robust economic dispatch

This paper proposes a novel frequency aware robust economic dispatch (FARED) approach to exploit the synergistic capability of accommodating uncertain loads and renewable generation by accounting for both the frequency regulation effect and optimal participation mechanism of secondary regulation reserves for conventional units in response to uncertainties in the robust optimization counterpart of security constrained economic dispatch.The FARED is formulated as a robust optimization problem.In this formulation the allowable frequency deviation and the possible load or renewable generation curtailments are expressed in terms of variable uncertainty sets.The variables in the formulation are described as interval variables and treated in affine form.In order to improve the computational tractability,the dominant constraints which canbe the candidates of tight transmission constraints are determined by complementarity constraints.Then the robust optimization problem is simplified to a bilinear programming problem based on duality theory.Finally,the effectiveness and efficiency of the proposed method are illustrated based on several study cases.

Distributionally Robust Transmission Expansion Planning Considering Uncertainty of Contingency Probability

The outage of power system equipment is one of the most important factors that affect the reliability and economy of power system.It is crucial to consider the influence of contingencies elaborately in planning problem.In this paper,a distributionally robust transmission expansion planning model is proposed in which the uncertainty of contingency probability is considered.The uncertainty of contingency probability is described by uncertainty interval based on the outage rate of single equipment.An epigraph reformulation and Benders decomposition are applied to solve the proposed model.Finally,the feasibility and effectiveness of the proposed model are illustrated on the IEEE RTS system and the IEEE 118-bus system.