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With the deepening of multi-energy flow coupling and the diversification of energy utilization demand, integrated energy system (IES) puts forward higher requirements for the economy and reliability of capacity allocation scheme. However, there is a complex relationship between economy and reliability, which makes it difficult for them to achieve coordinated optimization. Therefore, an economic and reliability optimal capacity allocation method of IES based on improved co-evolution algorithm is proposed. Firstly, the timing scenario reduction method of "wind power-electrical load" based on ordered clustering algorithm and K-means clustering algorithm is proposed. Aiming at minimizing the expected annual cost of construction investment, operation and maintenance, carbon emission and energy purchase cost, and constrained by equipment operation and energy flow balance, a multi-scenario economic operation model is constructed. Considering the dissatisfaction degree of comprehensive energy consumption, the energy supply reliability evaluation model based on Markov chain Monte Carlo (MCMC) method is established. On this basis, the IES multi-objective optimal capacity allocation model is established. Further, based on the orthogonal test method, the influence factors of decision variables on IES economy and reliability are calculated, and the evolution subpopulation of economy and reliability is established, and then the optimization model's solution method based on improved co-evolution algorithm is proposed. Finally, an example simulation analysis is carried out combined with the actual data of a certain region. The simulation results show that the proposed model can cooperatively improve the economy and reliability of IES capacity allocation scheme, and provide a reference for the optimal capacity allocation of equipment in IES. [ABSTRACT FROM AUTHOR]

Summary: With the increasing proportion of renewable energy generation connected to grid, the architecture, control method, and operation mode of the energy system begin to change. The energy system has some problems, such as difficult to maintain the privacy of energy suppliers, difficult to distinguish the authenticity of data, and low security and reliability. The introduction of blockchain technology into energy system to form an energy blockchain network is conducive to solving the information security and other issues. Aiming at the integrated energy system (IES) with renewable energy generation, the practical Byzantine fault tolerance (PBFT) algorithm consensus mechanism suitable for IES is proposed to realize the two‐stage robust optimal scheduling model, which uses decentralized scheduling strategy and blockchain technology. In the first stage, the technology of decoupling heat power is used to form electrical/thermal iteration chain, and the decentralized scheduling model based on Lagrange multiplier method is built. For the data consistency of decentralized scheduling, the undirected unicom structure is used to establish the communication network between energy suppliers. The thermal/electrical iterative chain and the terminal chain apply the consistency protocol algorithm and Byzantine fault tolerance consensus mechanism to solve the prescheduling scheme based on the decentralized algorithm. In the second stage, the blockchain technology is used to obtain historical data, and a data‐driven renewable generation power uncertain set is established to solve the Regulation scheme. This constraint set can exclude some extreme scenarios to reduce the conservatism of the model. In the two‐stage optimization process, the verification function of Byzantine fault tolerance consensus mechanism is used to discard the information tampered by malicious attack and enhance the system fault tolerance capability. The example verification results show that the proposed architecture can operate effectively and save total cost of scheduling solution 5.71%, prevent the energy suppliers from tampering with information and weaken the impact of server downtime and network errors, and improve the privacy, security, and reliability of operation scheduling. [ABSTRACT FROM AUTHOR]

At present, the traditional load model relies on a large number of historical data, ignoring the relationship between load demand and user behavior and the temporal and spatial distribution of load. To solve this problem, this paper proposes a modeling method of energy consumption characteristics of commercial office building considering the physical characteristics of energy consuming equipment and staff behavior factors. Firstly, the load of commercial office building is classified based on user behavior. Secondly, according to the proposed load classification method, a time distribution model associated with user behavior is established for each type of load energy consumption equipment, and the total load model of commercial office building is obtained. On this basis, the proposed model is expanded in both time and space, so that the total load model of commercial office building can be used to analyze energy consumption in different time scales and different regional areas. Through the combination of non-intrusive load decomposition and Markov chain, the energy consumption behavior of users is analyzed and simulated, and a refined load forecasting method of commercial office building considering equipment and user behavior under demand response mechanism is proposed. Finally, the example analysis shows that the proposed method can no longer depend on huge amounts of similar data for driving, can effectively reduce the impact of the original data on the load feature extraction, and has the ability to achieve load forecasting independently. [ABSTRACT FROM AUTHOR]