With the growing proportion of advanced metering infrastructures and intelligent controllable equipment in power grids, demand response has been regarded as an effective and easily implemented approach to meet the demand–supply equilibrium. This paper innovatively proposes generalized demand-side resources combining the demand response with an energy storage system and constructs a configuration model to obtain scheduling plans. Firstly, this paper analyzes the characteristics of generalized demand-side resources and models the translational loads, reducible loads and energy storage system. Secondly, a deterministic energy storage configuration model aiming at achieving the lowest operation cost of distribution networks is established, from which the scheduling scheme of generalized demand-side resources can be obtained. Then, the fuzzy membership function and the probability density function are used to represent the uncertainty of the demand response, the prediction error of renewable energy output and the generalized demand-side resources that do not participate in the demand response. Therefore, this paper simulates daily operations to modify the capacity of energy storage. The problem is solved by using Monte Carlo simulation, fuzzy chance-constrained programming and mixed-integer programming. Finally, the effectiveness of this model is demonstrated with case studies in a 33-node distribution network. The results show that the uncertainty of this system is solved effectively. When only considering generalized demand-side resources, the total cost is reduced by 9.5%. After considering the uncertainty, the total cost is also decreased 0.3%. Simultaneously, the validity of the model is verified.

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The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system. Mobile energy storage systems (MESSs) have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability. However, when urban traffic flows rise, the congested traffic environment will prolong the transit time of MESS, which will ultimately affect the operation state of the power networks and the economic benefits of MESS. This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks, considering road congestion and the operation constraints of the power networks. The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator (DNO) in order to maximize the total revenue of the system. With fuzzy time windows and fuzzy road congestion indexes, the lower-level model optimizes the route for the transit problem of MESS. Therefore, road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme. Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model. The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion. Besides, the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

As a retailer between the energy suppliers and end users, the integrated energy service provider (IESP) can effectively coordinate the energy supply end and the energy use end by setting energy prices and energy management. Because most of the current research focuses on the pricing of electricity retailers, there are few studies on IESP energy pricing and management, which are still at the initial stage. At the same time, the existing research often does not consider the impact of demand response (DR) and uncertainties, such as natural gas and electricity wholesale prices, on the pricing of IESP. It is necessary to model the DR and uncertainties in the integrated energy system. Aiming at the inadequacy of the existing research and to address the energy pricing and management of IESP, this paper develops a two-stage stochastic hierarchical framework, which comprehensively considers the DR strategy of the user end, characteristics of the electricity/gas/heat storage and the uncertainties of electricity and gas wholesale prices. The proposed hierarchical model for energy pricing and management is a two-layer model: the upper layer is the problem of maximizing the benefits of IESP, and the lower layer is the problem of minimizing the energy cost of user agents. Through the complementary transformation, the linearization method and the strong duality principle in the optimization theory, the model is transformed into a mixed-integer linear programing (MILP) problem, which can be easily solved by the off-shelf commercial solver. Finally, the simulation results are provided to demonstrate the interactive operation between the IESP and user agent through energy prices setting, DR strategy and energy management.

Bulk power grid interconnection and the access of various smart devices make the current grid highly complex. Timely and accurately identifying the power grid operation state is crucial for monitoring the operation stability of the power grid. For this purpose, an evaluation method of the power grid operation state based on random matrix theory and qualitative trend analysis is proposed. This method constructs two evaluation indicators based on the operation data of the power grid, which cannot only find out whether the current state of the power grid is stable but can also find out whether there is a bad operation trend in the current power grid. Compared with the traditional method, this method analyzes the power grid’s operation state from the big data perspective. It does not need to consider the complex network structure and operation mechanism of the actual power grid. Finally, the effectiveness and feasibility of the method are verified by the simulations of the IEEE 118-bus system.