Sustainable energy development has gained worldwide attention, in part thanks to the wind power industry value chain that focuses on overall value creation and innovation, especially in China. This paper aims to construct a wind power industry value chain model and comprehensively analyze factors that have significant influences on it using a modified diamond model, which has remained nebulous. Focused on the value-adding effect of constructed value chains, we offer key ideas from different angles. A factor condition lays the foundation of the value chain, and shows that China is experiencing energy structure adjustment in which wind power will play a key role; its resource potential is huge, but with mismatched distribution. Demand conditions reveal an increasing demand for wind but serious wind rejection as well; this is where the value-adding probability exists. Related and support departments collaborate to determine the overall value creation process. Firm strategy, structure, and rivalry are terms that describe possible value-adding subjects considering the wind industry as a whole. Government and opportunity provide robust prices and non-price policies to support value integration, and Technology is an effective factor in cost reduction and value creation as a high value-adding sector. Furthermore, a comparison of wind power industry value chains in China and Japan is conducted. Our findings underscore that a gap exists between actual performance and the expected wind power industry value chain, and corresponding measurements to promote the performance are discussed, including encouraging diversified business models, enhancing R&D and independent innovation, professional cultivation, effectively reducing wind rejection rate, and the full range of government support.

The optimal operation of microgrids is a comprehensive and complex energy utilization and management problem. In order to guarantee the efficient and economic operation of microgrids, a three-layer multi-agent system including distributed management system agent, microgrid central control agent and microgrid control element agent is proposed considering energy storage units and demand response. Then, based on this multi-agent system and with the objective of cost minimization, an operation optimization model for microgrids is constructed from three aspects: operation cost, environmental impact and security. To solve this model, dynamic guiding chaotic search particle swarm optimization is adopted and three scenarios including basic scenario, energy storage participation and demand response participation are simulated and analyzed. The results show that both energy storage unit and demand response can effectively reduce the cost of microgrid, improve the operation and management level and ensure the safety and stability of power supply and utilization.

The rapid development of Energy Internet (EI) has prompted numbers of generators to participate, leading to a hybrid power system. Hence, how to plan the hybrid power system and allocate its profit becomes necessary. In this paper, the cooperative game theory is introduced to discuss this problem. We first design the basic structure of EI, and point out the object of this study—coal power plant-wind farm-photovoltaic power station-energy storage provider (CWPE) alliance. Subsequently, average allocation strategy (AAS), capacity-based allocation strategy (CAS) and Shapley value allocation strategy (SAS) are proposed, and then the modified disruption propensity (MDP) index is constructed to judge the advantages and disadvantages of the three schemes. Thirdly, taking a certain area of A Province as an example, the profits of CWPE under three strategies are calculated respectively. Finally, by analyzing individual rationality and collective rationality of cooperative game and the MDP index of the three profit allocation schemes, we find that SAS is the most stable.

With the rapid development of energy Internet (EI), energy storage (ES), which is the key technology of EI, has attracted widespread attention. EI is composed of multiple energy networks that provide energy support for each other, so it has a great demand for diverse energy storages (ESs). All of this may result in energy redundancy throughout the whole EI system. Hence, coordinating ESs among various energy networks is of great importance. First of all, we put forward the necessity and principles of energy storage coordination (ESC) in EI. Then, the ESC model is constructed with the aim of economic efficiency (EE) and energy utilization efficiency (EUE) respectively. Finally, a multi-agent particle swarm optimization (MAPSO) algorithm is proposed to solve this problem. The calculation results are compared with that of PSO, and results show that MAPSO has good convergence and computational accuracy. In addition, the simulation results prove that EE plays the most important role when coordinating various ESs in EI, and an ES configuration with the multi-objective optimization of EE and EUE is concluded at last.