Demand-side Scheduling Research Articles

Demand Side Response Participation in Reserve Configuration Optimization Based on Decomposition and Coordination

As the proportion of new energy sources in the grid increases and the structure of the ultra-high-voltage grid becomes more complicated, the variability of the external environment and the complexity of the grid itself bring many new challenges to the power system reserve configuration problem. The multilevel and multitime scale characteristics of the grid reserve are analyzed in this paper, and the reserve is classified according to the response time characteristics combined with the system function. We establish a demand-side response participation system reserve configuration optimization model, including synchronous generator generation and reserve models, demand-side scheduling and reserve models, grid power generation planning and reserve optimization mathematical models. Based on the idea of decomposition coordination, the current power generation plan and reserve optimization problem are decomposed into a finite fault state safety constraint unit combination subproblem, Monte Carlo secondary negative reserve constraint check subproblem, frequency safety constraint checking subproblem based on large-disturbance long-process time domain simulation analysis, and fault state static safety constraint check subproblem based on minimum load shedding. Considering the demand side response to the reserve configuration of the system, combined with the problem of previous unit association, the optimal allocation of the reserve coal consumption units is realized by determining the start and stop status of the unit and the unit output.

Optimal Multi-Timescale Demand Side Scheduling Considering Dynamic Scenarios of Electricity Demand

In this paper, an optimal multi-timescale demand side scheduling framework, i.e., the combination of week-ahead and day-ahead, for industrial customers is proposed. Different demand side management (DSM) techniques suitable for distinct week-ahead and day-ahead timescales cooperate for achieving the overall optimal demand scheduling in the entire multi-timescale frame. Specifically, in the week-ahead scheduling, a dynamic scenario generation method is proposed to accurately simulate uncertainties of customer electricity demand time-series during the scheduling horizon, which can represent not only the marginal distribution of possible customer loads at each time instant but also the joint distribution among multiple loads at different time instants. In addition, priorities of various DSM techniques accepted by DSM participants and their willingness are also considered, aiming at mitigating impacts on their normal manufacturing process. With actual historical load data of industrial customers from advanced metering infrastructure system, the dynamic scenario generation method is shown to be effective in preserving statistic features of load fluctuations, and the proposed optimal multi-timescale coordinated demand side scheduling model is demonstrated to be an effective DSM approach.

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.

Multi-objective demand side scheduling considering the operational safety of appliances

The safe operation of appliances is of great concern to users. The safety risk increases when the appliances are in operation during periods when users are not at home or when they are asleep. In this paper, multi-objective demand side scheduling is investigated with consideration to the appliances’ operational safety together with the electricity cost and the operational delay. The formulation of appliances’ operational safety is proposed based on users’ at-home status and awake status. Then the relationships between the operational safety and the other two objectives are investigated through the approach of finding the Pareto-optimal front. Moreover, this approach is compared with the Weigh and Constraint approaches. As the Pareto-optimal front consists of a set of optimal solutions, this paper proposes a method to make the final scheduling decision based on the relationships among the multiple objectives. Simulation results demonstrate that the operational safety is improved with the sacrifice of the electricity cost and the operational delay, and that the approach of finding the Pareto-optimal front is effective in presenting comprehensive optimal solutions of the multi-objective demand side scheduling.