Day-ahead Optimal Scheduling Model Research Articles

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

Under the dual pressure of energy shortage and environmental pollution, relying only on increasing the installed capacity of units and line transmission capacity cannot cope with the conflict between the growth of power demand and the difficulty of grid expansion in the long run. Demand response conducts users to change their energy consumption habits through system-issued electricity prices or incentives, so that the demand of the load side can be adjusted flexibly, which can further enhance the consumption of wind power and improve system economics. Based on the background of diversified energy use, this paper proposes a day-ahead optimal scheduling strategy for integrated electricity and thermal system considering multiple types of demand response. Firstly, the dispatch framework of integrated electricity and thermal system with the situation awareness technology is constructed to address uncertainties of Renewable Energy Sources, thus helping system mitigate uncertain risks. Secondly, the demand response mechanism of power system and regional thermal inertia of thermal system are modeled, respectively, to uncover the principles of load regulation of different energy systems; Then, a day-ahead optimal scheduling model for the integrated thermal and electricity system is developed, and the consumption evaluation index is integrated to indicate energy utilization efficiency; Finally, a combined electric-heat system model with 39-node grid and 6-node heat network is developed, and the positive effects of considering multiple types of demand response and situation awareness technology on promoting the consumption of renewable energy and improving the energy efficiency of the system are verified through the case study.

Research on Day-Ahead Optimal Scheduling Considering Carbon Emission Allowance and Carbon Trading

In the context of the marketization of carbon trading in the power system, it is of great theoretical and practical significance to study a scientific and effective carbon emission quota allocation strategy. To solve this problem, under the current situation of large-scale access to new energy, considering the limitations of the carbon emissions from different emission subjects plus the construction of a carbon trading model among the emission subjects, a day-ahead optimal scheduling method that takes carbon emission quotas and carbon trading into account is proposed. Firstly, carbon transaction cost models of thermal power and wind power are constructed, respectively, and a carbon emission quota allocation strategy based on the entropy method is proposed to redistribute the weights of baseline emission factors for the regional power grid. Then, considering the additional carbon emissions of conventional thermal power units caused by wind power access, the carbon trading costs of different types of generation units are calculated on the basis of carbon trading price prediction. Thereafter, a day-ahead optimal scheduling model considering carbon emissions trading is constructed with the objective of minimizing the total cost of the system in the scheduling period. The model is solved as an MINLP problem based on MATLAB 2016a software utilizing CPLEX 12.4. Simulation results verify the correctness and effectiveness of the proposed method.

Day-ahead optimal scheduling model of transmission–distribution integrated electricity–gas systems based on convex optimization

With the deepening of the coupling degree between power network and natural gas network, the traditional independent optimal scheduling of transmission and distribution integrated electricity–gas systems cannot make full use of the flexible control ability of various resources and the complementary support capabilities between different energy systems. Therefore, this paper puts forward a cooperative optimization scheduling model of transmission–distribution​ integrated electricity–gas systems (TD-IEGS). The coordinated operation of multi-energy coupling devices including gas-fired units, combined heat and power (CHP) units and electric boilers (EB) are considered in this paper to enhance system operation flexibility and economy. The second order cone relaxation (SOCR) method is utilized to tackle the non-linear power and gas flow constraints, and the proposed mixed-integer non-linear programming co-optimization scheduling model is transformed into a mixed integer second order cone programming model (MISOCP). Case studies demonstrate that compared with the independent operation mode, the proposed co-optimization scheduling model of TD-IEGS can reduce total operation cost by about 6.7% under 50% wind power penetration level. Moreover, the co-optimization of TD-IEGS can promote wind power utilization especially in a high share of wind energy.

Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm

The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs is an ideal method to solve this problem. In this study, a day-ahead optimal scheduling model for IES that included an electrical system, a natural gas system, and an energy hub (EH), was established. The proposed EH contained detailed models of the fuel cell (FC) and power to gas (P2G) system. Considering that the optimal scheduling of an IES is a non-convex complex optimal problem, a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on multistage chaotic mapping was proposed to solve it. The objective was to minimize the operating cost of the IES. Three operation scenarios were designed to analyze the operation characteristics of the system under different coupling conditions. The simulation results showed that the PCAPSO algorithm improved the convergence rate and stability compared to the original PSO. An analysis of the results demonstrated the economics of an IES with the proposed EHs and the advantage of cooperation between the FC and P2G system.

A Day-Ahead Scheduling Model of Electricity-Gas Integrated System with Power-to-Gas

In recent years, with the increasing of gas-fired power plants and the development of power-to-gas (P2G) technology, and the interdependence between the power system and the natural gas system has gradually deepened. This paper proposes a day-ahead optimal scheduling model for an electricity-gas integrated system. Based on P2G, it can realize the two-way movement of energy between the power system and the natural gas system, promote the coordinated and optimized operation of the two energy systems, and improve energy utilization. The model proposed in this paper minimizes the total cost of both systems. Firstly, the power system sub-model and the natural gas system sub-model in the collaborative optimization model are studied separately, and the constraints between the two systems are considered. Then, the piecewise linearization method and DC power flow simplification method are adopted, the nonlinear problem is transformed into a mixed integer linear programming problem. Finally, the load forecast value of the day-ahead dispatch is loaded, and the 24-hour dispatch result is obtained through the simulation platform, and the P2G is used for joint dispatch.

Improved MOEA/D approach to many-objective day-ahead scheduling with consideration of adjustable outputs of renewable units and load reduction in active distribution networks

Within the increasing concerns on environment, economy and security and requirements on power quality, the conventional economic dispatching scheme will face challenges in the optimizing objectives and constraints of the day-ahead optimal scheduling of active distribution network (ADN). This paper proposes a many-objective (the number of objectives is more than three) day-ahead optimal scheduling model for the ADN. With consideration of adjustable outputs of renewable energy units and reducible load, four objectives including the minimization of total operating cost, minimization of active network loss, minimization of voltage deviation and minimization of the total output reduction rate of renewable energy are involved satisfying various constraints such as component constraints, load constraints and network constraints. An improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is put forward to solve this many-objective optimization problem. In the proposed approach, normalizing objective functions is firstly implemented before calculating the aggregation function in order to reduce effects of different magnitude orders of objectives. Then, a new aggregation method is utilized instead of the conventional Tchebyshev aggregation method to calculate the improvement rate, which is the basis for allocations of computing resources. The new aggregation function utilizes the weighted sum of horizontal distance and vertical distance between the subproblem and the corresponding weight factor, in which not only convergence but also diversity is considered. Moreover, a dynamic neighborhood replacement strategy is also employed to avoid the mismatch between generated solutions and subproblems and to balance the population diversity and convergence. The effectiveness of the proposed model and the improved MOEA/D are verified by the simulation analysis of the IEEE33-bus power system.

Day-ahead Optimal Scheduling Model of Integrated Energy System in Industrial Parks Considering Energy Storage Characteristics of Cooling-supply Areas in Buildings

Day-ahead Optimal Scheduling Model of Integrated Energy System in Industrial Parks Considering Energy Storage Characteristics of Cooling-supply Areas in Buildings

Multi-time scale energy management of electric vehicle model-based prosumers by using virtual battery model

The increasing number of small and medium-sized prosumers with distributed energy resources (DERs) has led to the need for innovative operational strategies at the distribution system level. Among them, microgrid (MG) energy management for peer-to-peer power sharing between prosumers is a promising approach. In this study, we develop a multi-time scale optimization method for virtual battery model-based prosumer energy management. First, a day-ahead optimal scheduling model is established based on an integrated virtual battery model that aims to minimize the total dispatching cost of a prosumer-oriented MG without impacting the privacy of individual end-prosumers. Then, close to real-time operation, model predictive control is applied to minimize the deviation between the real-time power and the day-ahead optimal schedule over the control horizon for prosumers with energy storage resources. The simulation results show that the proposed approach employing the integrated virtual battery model to quantitatively characterize the resource flexibility of prosumers yields a good optimization performance and high computing efficiency, compared to those of the approaches modelling traditional DER flexibilities.

An economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response

Demand response (DR) and wind power are beneficial to low-carbon electricity to deal with energy and environmental problems. However, the uncertain wind power generation (WG) which has anti-peaking characteristic would be hard to exert its ability in carbon reduction. This paper introduces DR into traditional unit commitment (UC) strategy and proposes a multi-objective day-ahead optimal scheduling model for wind farm integrated power systems, since incentive-based DR can accommodate excess wind power and can be used as a source of system spinning reserve to alleviate generation side reserve pressure during both peak and valley load periods. Firstly, net load curve is obtained by forecasting load and wind power output. Then, considering the behavior of DR, a day-ahead optimal dispatching scheme is proposed with objectives of minimum generating cost and carbon emission. Non-dominated sorting genetic algorithm-II (NSGA-II) and satisfaction-maximizing method are adopted to solve the multi-objective model with Pareto fronts and eclectic decision obtained. Finally, a case study is carried out to demonstrate that the approach can achieve economic and environmental aims and DR can help to accommodate the wind power.