Multi-objective Dispatch Model Research Articles

A novel two-stage multi-objective dispatch model for a distributed hybrid CCHP system considering source-load fluctuations mitigation

Small-scale distributed energy systems with combined cooling, heating, and power (DES-CCHP) production have attracted international interest. However, fluctuating loads and renewable energies continuously disturb the real-time operation of DES-CCHP and even the connected grid, hindering the broad application of grid-connected DES-CCHP. In this paper, a novel model predictive control (MPC) based two-stage strategy is developed for DES-CCHP, with multiple timescales consideration. In the first stage, a multi-objective MPC is built with inheritance and fusion mechanisms of multi-step scheduling instructions, simultaneously minimizing operation costs and power fluctuations. The second stage performs intelligent decision-making on the results of the first stage, which allows appropriate state-switching instructions to ensure economy. Meanwhile, the decision-making rejects the unnecessary one and triggers a second-round flexibility-based interventional optimization to revise scheduling planning. The case studies show that compared to dispatch without MPC, the economic single-objective MPC, single-stage multi-objective MPC (only applies the first-stage models), and two-stage multi-objective MPC save costs by 5.31 %, 4.80 %, and 4.57 %, respectively. As for fluctuations mitigation, the single-stage and two-stage models significantly smooth power fluctuations by 51.66 % and 69.93 %, respectively. The proposed strategy operates DES-CCHP economically, stably, and grid-friendly under a rugged operating environment.

Selection of an economics-energy-environment scheduling strategy for a community virtual power plant considering decision-makers’ risk attitudes based on improved information gap decision theory

To address the impact of decision-makers' risk attitudes (DMRA) and uncertainties on dispatch strategy in community integrated energy systems, a multi-objective dispatch model of a community virtual power plant (CVPP) is proposed. Firstly, a novel CVPP model that considers DMRA and a multi-objective satisfaction model of economics-energy-environment are built. Secondly, the information gap decision theory (IGDT) model is improved by considering uncertainties of renewable energy, loads, and DMRA. Thirdly, the VIKOR method is improved by using the weights from multiple objective functions derived by combining the entropy weight and full consistency method. And the conservative and risky strategy selection methods in the VIKOR method are used to select the optimal strategy from the obtained multi-objective Pareto solution set that satisfies decision-makers with risk preferences. Thus, the impact of DMRA on optimal strategy selection is fully described. Finally, the proposed model's effectiveness is validated using a multi-scenario example of a residential area. The results indicate that: 1) The novel CVPP provides realistic scheduling strategies based on the DMRA. 2) After implementing demand response, the resident cost and net carbon emissions are reduced by 9 % and 91 %, respectively. Energy supplier profit and renewable energy utilization rate are increased. The constructed IGDT model also improves multiple objectives. 3) The improved IGDT model's uncertainty and deviation factors allow for diverse scheduling strategies. Simultaneously, the improved VIKOR method provides a new method for decision-makers to select strategies. The model is a guide for selecting scheduling strategies and a way of encouraging the usage of renewable energy.

Event-triggered distributed control strategy for multi-energy systems based on multi-objective dispatch

As environmental protection greatly influences the social development, for the multi-energy systems (MES) equipped with a cluster of energy devices, the economic dispatch (ED) problem should not only be considered but also the environmental protection problem should be considered in energy utilization. To address this issue, a multi-objective dispatch model of MES using a linear weighted sum algorithm (LWS) is developed in this paper, which considers the environmental and economic costs. On this basis, a fully distributed algorithm with the coupled control mechanism of power and heat is presented to realize coordination optimization between the environmental and economic benefits. Furthermore, an event-triggered communication strategy is implemented in the fully distributed algorithm, which can be effectively applied to the multi-objective dispatch model, to reduce the communication burden. Finally, the simulation results verify the effectiveness of the proposed distributed control strategy.

Enhanced evolutionary multi-objective optimization-based dispatch of coal mine integrated energy system with flexible load

Integrated energy systems, especially park-level ones, have attracted great attention since such structures lay the foundation of multi-energy complementary and cascade utilization of energy. Integrated energy systems for industries with associated multiple energy generated during the production process have not been explored. Here, we focus on the integrated energy systems for mine industry due to its high ecological requirements, i.e., the associated underground wastewater, mine gas, ventilation air methane, and geothermal should be sufficiently recycled and released as little as possible into nature. To this end, we first present a structure for coal mine integrated energy systems by integrating these forms of associated energy together with some flexible load. The multi-objective dispatch model of the system is then derived by considering the economic cost, carbon transaction cost for environment protection and degree of customer dissatisfaction for reducible and translational load. An enhanced evolutionary multi-objective algorithm by considering the time-series constrictions of the flexible load is further explored to efficiently obtain a set of solutions of the multi-objective dispatch. The presented algorithm is applied to a coal mine with multiple scenarios, and the results demonstrate the feasibility of our model and the effectiveness of the evolutionary multi-objective algorithm-based dispatch.

Optimal Time-of-Use Electricity Price for a Microgrid System Considering Profit of Power Company and Demand Users

With high proportions of renewable energy generation in power systems, the power system dispatch with renewable energy generation has currently become a popular research direction. In our study, we propose a multi-objective dispatch model for a hybrid microgrid comprising a wind generator, photovoltaic (PV) generator, and an energy storage system to optimize the time-of-use (TOU) electricity price. The objective of the proposed multi-objective dispatch model is to maximize the profit of the power company and demand users, and minimize the proportion of users abandoning PV power and wind power. The elastic price of the load demand with a linear function is employed to optimize the TOU electricity price. Finally, we applied five test cases to validate the practicability of the multi-objective dispatch model.

Simulated Annealing Algorithm for Dynamic Economic Dispatch Problem in the Electricity Market Incorporating Wind Energy

In this paper, a bid-based dynamic economic dispatch (BBDED) problem is solved in the electricity market system under bidding strategies, including wind energy penetration using simulated annealing (SA) algorithm. The multi-objective dispatch model allows generating companies (GENCOs) and their customers to submit supply and demand bids to a market controller known as the independent system operator (ISO) and follow a bidding strategy. ISO is responsible for the market clearing and settlement to maximize the social profit and benefit for GENCOs and customers during trading periods. To study the effect and advantages of wind energy integration in the BBDED problem, the wind energy generation is computed using the forecasted wind speeds and included in the dispatch model. In this regard, the ISO's dispatch model is formulated as a bilevel nonlinear optimization problem. The higher-level is solving the market-clearing with and without wind energy, and the lower level is maximizing GENCO's social profit. The proposed SA algorithm is evaluated for optimality, convergence, robustness, and computational efficiency tested on an IEEE 30-bus test system. The simulation results are compared with those found using different algorithm-based approaches, considering various constraints like power balancing, generator limits, ramp rate limits, and transmission losses.

Multi-objective fuzzy optimization model of power system dynamic environment economy based on coordinated dispatching of wind power and electric vehicles

In order to coordinate the impact of the integration of electric vehicles and wind power on the economic dispatch of power systems, a multi-objective dispatch model for the environmental economy of wind farms with PHEVs connected to the grid was constructed. The uncertain factors of wind power generation and load will increase the difficulty of dispatching. The fuzzy theory will be used to realize the fuzzy modelling of the coordinated connection of wind power and electric vehicles. The satisfaction index will be used to transform the multi-objective problems of deterministic environment and economy into single For the objective optimization problem, based on the improvement of the traditional particle swarm optimization algorithm using linear descent search ideas and the particle information sharing method considering boundary constraints, the improved PSO algorithm is used to solve the proposed dynamic environment and economic dispatch model. From the grid operation and vehicle owners from the perspective of demand, the constraints of the system’s rotation reserve and the daily travel of the owner are considered. The comparison and analysis of the simulation of algorithms and calculation examples and multiple scheduling schemes in different scenarios verify the rationality of the model and the superiority of the improved PSO algorithm for solving such dynamic scheduling problems.

Adaptive Robust Method for Dynamic Economic Emission Dispatch Incorporating Renewable Energy and Energy Storage

In association with the development of intermittent renewable energy generation (REG), dynamic multiobjective dispatch faces more challenges for power system operation due to significant REG uncertainty. To tackle the problems, a day‐ahead, optimal dispatch problem incorporating energy storage (ES) is formulated and solved based on a robust multiobjective optimization method. In the proposed model, dynamic multistage ES and generator dispatch patterns are optimized to reduce the cost and emissions. Specifically, strong constraints of the charging/discharging behaviors of the ES in the space‐time domain are considered to prolong its lifetime. Additionally, an adaptive robust model based on minimax multiobjective optimization is formulated to find optimal dispatch solutions adapted to uncertain REG changes. Moreover, an effective optimization algorithm, namely, the hybrid multiobjective Particle Swarm Optimization and Teaching Learning Based Optimization (PSO‐TLBO), is employed to seek an optimal Pareto front of the proposed dispatch model. This approach has been tested on power system integrated with wind power and ES. Numerical results reveal that the robust multiobjective dispatch model successfully meets the demands of obtaining solutions when wind power uncertainty is considered. Meanwhile, the comparison results demonstrate the competitive performance of the PSO‐TLBO method in solving the proposed dispatch problems.

Flexibility-based improved model of multi-energy hubs using linear weighted sum algorithm

The formation and development of the energy internet through energy hubs enhance energy efficiency and improve system operating flexibility to accommodate more renewable energy sources (RESs) under the pressure of energy crisis. To further tap the potential of energy complementary and optimize the total operating cost in an operational model of multi-energy hubs, an improved energy management system model for the energy conversions of flexibility-based energy hubs using the linearized coupling relationship is proposed in this paper, which could realize regional coordination optimization and high proportion of RES consumption. Furthermore, an algorithm, which combines a Linear Weighted Sum algorithm and the Kuhn-Tucker condition, is presented to utilize surplus energy among the different energy hubs rationally and solve the optimal energy management problem of the constrained nonlinear multi-objective dispatch model. Finally, the results of the simulation indicate that the total operating cost of multi-energy hubs participating in energy complementary is reduced by nearly 15.23% in summer and approximately 8.56% in winter, and the proposed approach can successfully reduce the waste of RES.