The comprehensive energy-saving potential evaluation method of the energy-saving schemes of a distribution network considering the power uncertainty of source and load is studied in this paper. The K-means clustering method is firstly employed to extract typical scenarios of distribution network, and the forward-push back method is used to calculate the power flow in typical scenarios, then energy-saving reconstruction schemes are formulated according to the power flow calculation results. A comprehensive energy saving potential evaluation index system that consists the improvement rate of network loss, line loss rate, transformer loss rate, annual electricity saving cost, annual equipment investment cost, annual maintenance cost and voltage quality improvement rate is built, and the comprehensive evaluation method based on DEMATEL-ANP-TOPSIS mixed decision model is used to evaluate the comprehensive energy-saving potential of the energy-saving reconstruction schemes regarding the index system. Finally, the optimal reconstruction scheme is selected based on the evaluation results of energy saving potential in multiple scenarios. The effectiveness of the proposed method is verified by an example in IEEE-33 node distribution network.
Taking into account the energy cost, pollution emission, wind power consumption, and other dispatching objectives in the regionally integrated energy system (RIES), the RIES multi-objective optimization model considering the integrated demand response is established. Firstly, the RIES modeling of equipment including electricity-to-gas, energy storage systems, cogeneration units, etc., and the introduction of a comprehensive demand response that specifically considers load reduction, load transfer, and load replacement in the region, aimed at reducing system load peaks and valleys difference. Then, the objective function to minimize the system energy cost, the abandoned wind power, and the pollutant treatment cost was established respectively, and the multi-objective optimization method was adopted—the Pareto front was solved by fuzzy weighted programming traversal weights, and then the decision was made based on evidence Method to find the optimal scheduling strategy. Finally, based on a typical case study, the results show that the proposed multi-objective optimization algorithm can effectively make trade-offs among multiple scheduling objectives, and RIES considering comprehensive demand response has advantages in terms of total energy consumption, environmental friendliness, and wind power consumption.
The planning of integrated energy system is a very complex multi-target, multi-constraint, nonlinear, random uncertainty mixed integrated combination optimization problem, its planning and design process should not only consider the interdependence between the system capacity, energy conversion, energy storage, energy use and other links, but also consider the interaction and integration of cold, hot, electricity and other multi-energy flows, which is essentially a non-deterministic polynomial difficult problem. China’s energy continues to develop rapidly, all kinds of sensors and intelligent equipment data is increasing, the data obtained in the equipment and all kinds of sensors collected energy load prediction related factors such as temperature, weather, wind speed and other data volume increased dramatically, the data dimension is also increasing, the scale of data has also increased from GB to TB or even higher, based on the traditional prediction methods and intelligent prediction methods, has been far below the load forecast desired to achieve accuracy and speed requirements, Therefore, the use of big data technology to predict energy demand is an important future direction.
The planning of integrated energy system is a very complex multi-objective, multi-constraint, nonlinear, random uncertain hybrid combination optimization problem, its planning and design process should consider not only the system capacity, energy exchange, energy storage, energy and other links between the interdependence, but also the interaction and mixing of cold, hot, electricity and other multi-energy flow, which is essentially a non-deterministic polynomial problem. Based on load prediction technology, combined with scene generation, multi-interconnected energy system modeling and other technologies, around the integrated energy system planning and design, consider the comprehensive evaluation of the whole life cycle, an optimal configuration of the integrated energy system is formed.
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