Lidong Zhou's research while affiliated with State Grid Electric Power Research Institute and other places

Publications (6)

Article
Day-ahead electricity price forecasting (DAEPF) plays a very important role in the decision-making optimization of electricity market participants, the dispatch control of independent system operators (ISOs) and the strategy formulation of energy trading. Unified modeling that only fits a single mapping relation between the historical data and futu...
Article
The optimized operation of building energy management system (BEMS) is of great significance to its operation security, economy and efficiency. This paper proposed a day-ahead multi-objective optimization model for a BEMS under time-of-use (TOU) price based demand response (DR), which integrates building integrated photovoltaic (BIPV) with other ge...
Article
Full-text available
The optimal dispatching model for a stand-alone microgrid (MG) is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditi...
Article
Full-text available
The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT), a multi-objective op...

Citations

... Given its excellent performance in many applications, SVC is adopted in this paper [56,57]. The traditional SVC is a two-classifier that divides objects into two categories that result in the minimum of generalization errors. ...
... Conversely, studies proposing new ML methods only compare them with simple statistical methods [12][13][14][15][16] and show that ML models are more accurate. • In many of the existing studies [17][18][19][20][21][22][23] the testing periods are too short to yield conclusive results. In some cases, the test datasets Similar problems arise in the context of hybrid methods. ...
... Moreover, Forecasting of PV power is used to estimate the output power of PV stations from one side and load demand from the other side to guarantee effective energy management (Massaoudi et al., 2021). Forecasting of PV power is one of the most economical and feasible solutions and supports other solutions or technologies (Wang, Zhou, et al., 2018) like power flow optimization (Biswas, Suganthan and Amaratunga, 2017) and energy storage (Wang, Zhou, et al., 2018). However, Accurate forecasting of PV power could be a complex task due to PV power time series that display non-linear and unstable characteristics, and unpredictable meteorological conditions that PV power generation relies on (Li et al., 2018). ...
... Based on the NGA good global search ability and high convergence speed, the NGA is introduced to VMD to optimize the selection of the optimal parameter combination of the α and K values. At the same time, genetic operators are used before selection as an elite retention strategy to ensure the convergence of the NGA, retain the best genes to the greatest extent, and avoid the loss of elite individuals [24]. ...
... The article [23] proposed a multi-objective optimization model to the Building Energy Management System (BEMS) with a demand response based on time-of-use price in the YALMIP toolbox, MATLAB, integrating photovoltaic energy generation (BIPV) and other energy sources to optimize the energy reduction and environment comfort. Godina et al., in an article [24], presents an alternative method to reducing residential energy consumption by implementing the Model Predictive Control (MPC). ...
... The Pareto approach to multi-objective optimization is the key concept to establish optimal set of design variables, since the concepts of Pareto dominance and optimality are straightforward tools for determining the best trade-off solutions among conflicting objectives [32][33][34]. Pareto optimum solutions using a multi-objective genetic algorithm [30,32] for the combined simple Brayton and Rankine cycle (config.1), combined regenerative Brayton and Rankine cycle (config.2) and new combined regenerative Brayton and Rankine cycle (config.3) ...