Regional smart energy system optimization configuration method and system
A technology of smart energy and configuration methods, applied in the directions of resources, data processing applications, forecasting, etc., can solve problems such as the lack of urban smart energy system planning methods
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Embodiment 1
[0028] The present invention provides a method for optimal configuration of a regional smart energy system, such as figure 2 As shown, the method includes:
[0029] Step 101, obtaining the load demand of cold, heat and electricity in the area at each time in the optimization period;
[0030] Step 102, substituting the load demand of cooling, heating and electricity in the region at each time in the optimization cycle into the pre-built optimal configuration model, using the improved particle swarm optimization algorithm to solve the optimal configuration model, and obtaining the parameters to be optimized Optimal solution;
[0031] Among them, the improvement of the improved particle swarm optimization algorithm is to determine the similarity between each particle in the particle swarm and the optimal particle of the group, and divide the particles in the particle swarm into different subgroups according to the similarity, and according to each The swarm optimal particle of...
Embodiment 2
[0123] The present invention provides a regional smart energy system optimization configuration system, such as Figure 4 As shown, the system includes:
[0124] The obtaining module is used to obtain the load demand of cold, heat and electricity in the area at each time in the optimization cycle;
[0125] The optimization solution module is used for substituting the load demand of cold, heat and electricity in the region at each time in the optimization period into the pre-built optimal configuration model, using the improved particle swarm optimization algorithm to solve the optimal configuration model, and obtaining the Optimal solution of optimized parameters;
[0126] Among them, the improvement of the improved particle swarm optimization algorithm is to determine the similarity between each particle in the particle swarm and the optimal particle of the group, and divide the particles in the particle swarm into different subgroups according to the similarity, and accordi...
Embodiment 3
[0191] All users in a city's smart energy system are residential users. It is assumed that the heating / cooling pipelines of the district energy subsystem are laid close to the heating / cooling load nodes in the area, so the pipeline construction cost is not considered. Three energy subsystems are planned and constructed in the region. The energy production equipment of each regional energy subsystem is mainly photovoltaic, fan, cogeneration, gas boiler, electric boiler, electric refrigerator, absorption refrigerator, and the energy storage equipment is mainly For batteries and heat accumulators, the design life of the energy subsystem is 30 years. The voltage level of the city's smart energy system is 10kV, and the node load and line parameters are shown in Table 1.
[0192] Table 1
[0193] node 1 Load (MW+jMVar) node 2 Line Impedance (p.u.) 1 0.2+j0.116 13 0.02+j0.016 2 0.5+j0.125 1 0.0082505+j0.019207 3 0.8+j0.4 1 0.0082505+j0.019207 4...
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