Concentrating solar power (CSP) plant, which has great controllability and scheduling flexibility, is a promising renewable energy power generation technology. The reasonable scheduling of a CSP plant can effectively reduce the operating cost of the integrated energy system (IES). In this context, a day‐ahead optimal coordinative operation model of the electric–thermal–gas IES considering CSP plant is proposed in this study with the objective function of minimising the operating cost of the IES. In the proposed model, the transmission delay characteristic of the thermal network is considered to reflect the large thermal inertia of the thermal network. Then, the incremental formulation for the piecewise linearisation and the second‐order cone relaxation method are utilised to transform the proposed non‐linear model into a mixed integer quadratically constrained programming model, which can be efficiently solved by CPLEX solver. The case study shows that the optimal operation model considering CSP plant has better performance in reducing the operating cost and promoting the wind power penetration when compared with the model considering photovoltaic. In addition, the scheduling flexibility of IES can be enhanced by the virtual thermal energy storage characteristic of the thermal network to reduce the operating cost and promote the wind power penetration of IES.
With the grid's evolution, the end-users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies.To design efficient and precise LF, information about various factors that influence end-users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal-Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to
With the rapid development of renewable energy generation and multi‐energy system technologies, reviewing and discussing the emerging power system restoration methods and key technologies suitable for renewable‐dominated electric power systems and the Energy Internet are important. Based on this, the backgrounds of renewable‐dominated electric power systems and the Energy Internet are first introduced here. Subsequently, the power system restoration process is divided into three phases: the black‐start, network reconfiguration, and the load restoration phases; relative restoration strategy research on these three phases is reviewed. Moreover, the boundaries between these three phases are occasionally not sufficiently apparent or even cross in most cases owing to the severity of blackouts and other various factors. Therefore, the key technologies for power system restoration considering multiple phases are analysed in detail. Moreover, the major gaps between existing research and real‐world applications and the outlooks on the restoration technologies under renewable‐dominated electric power systems are discussed.
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