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The performance of regional distributed energy systems is closely related to operation strategies and is also affected by the renewable energy penetration rate of the power system. According to the load characteristics of a public building in Changsha, a regional distributed energy system of a combined heating and power unit coupled with a photovoltaic, ground source heat pump, air-source heat pump, and energy storage is proposed to explore the impact of the power system's renewable energy penetration rate on its optimization and operation. Based on the following electric load, following thermal load, and following hybrid electric-heating load strategies, this paper conducts a multi-objective optimization design for the proposed system according to two scenarios, where Scenario 1 considers the change of renewable energy penetration in the power system, and Scenario 2 does not. According to the optimization results, the impact of the change in the power system's renewable energy penetration rate on the system performance is comparatively analyzed. The results show that the electricity consumption share of the system from Scenario 1 is greater than that of the system from Scenario 2, regardless of the design and operation conditions. With the increase of renewable energy penetration, the primary energy saving rate of the system from Scenario 1 is higher than that of the system from Scenario 2, with increasing rates of 37.60%, 7.92%, and 36.87% under three strategies, respectively. Furthermore, for Scenario 1, the system operation performance under the following thermal load strategy is better than that under the other two strategies. The results of this paper provide some guidance for the optimal design of grid-connected distributed energy systems. • A RDES is proposed based on building load characteristics. • A multi-objective optimization model considering the power system's renewable energy penetration rate is developed. • The impact of the power system's renewable energy penetration on the design and operation of the RDES is explored. [ABSTRACT FROM AUTHOR]

• A new hybrid model, namely CEEMDAN-SE-LSTM is proposed. • The model allows for more accurate forecasting of electrical loads. • The model is significantly better than models like EEMD-LSTM, CEEMDAN-LSTM, etc. Ultra-short-term power load forecasting refers to the use of load and weather information from the prior few hours to forecast the load for the next hour, which is very important for power dispatch and the power spot market establishment. Based on time series decomposition-reconstruction modeling and neural network forecasting, this study constructed a CEEMDAN- 1 1 May 13, 2014, was chosen as the starting time because the meteorological data (e.g., temperature, rainfall, wind speed, etc.) obtained by the authors for Changsha City started from that time; and to highlight the cyclical nature of the electricity load demand, given the lack of meteorological data afterApril 2018, May 13, 2017, was chosen as the cut-off date in this paper. However, we believe that the starting and ending time does not affect the idea and method of model construction in this paper. SE-LSTM model and used it to forecast the ultra-short-term electricity load in Changsha, China, considering meteorological and holiday factors. The article first decomposed the power load data from May 13, 2014, to May 13, 2017, at 24 time points per day for three years to obtain six component series, and then reconstructed them into a two-component series based on the sample entropy analysis to reflect the fluctuation and trend characteristics of the power load. Then, the LSTM neural network model was used to predict and superimpose the reconstructed component series to obtain the final prediction results. It was found that the RMSE, MAE, and MAPE of the CEEMDAN-SE-LSTM model were 62.102, 47.490, and 1.649 %, respectively, which were significantly better than those of the ARMA, LSTM single-prediction, EEMD-LSTM, and CEEMDAN-LSTM models. This study greatly improves the accuracy of ultra-short-term power-load forecasting, provides support for ultra-short-term power dispatching in Changsha, and provides a reference for other cities to develop short-term and ultra-short-term power load forecasting models. [ABSTRACT FROM AUTHOR]

Understanding the demand flexibility of city-scale buildings under different demand response (DR) strategies is crucial to balance the power supply and demand in the grid. This paper presented a case study to evaluate the electricity consumption reduction potential for buildings in Changsha under different DR strategies. First, sixty-six prototype energy models for twenty-two building types of three vintages were selected to represent 59,332 buildings in Changsha. The total electricity consumption of the building stock was obtained by aggregating the prototype electricity use intensity with their representative building area. Three DR strategies were formulated, including temperature reset, lighting control, and plug load control. Second, a toolkit, AutoBPS-DR, was developed to automatically integrate individual DR strategy as well as their combination into the baseline energy models. Five scenarios were analyzed, including the baseline case, three cases with a single DR strategy, and a case with three DR strategies combined. The results showed that compared to the baseline scenario, the electricity consumption reductions of the case buildings for a 3-h DR event under temperature reset, lighting control, plug load control, and combined control scenarios were 603.97 MWh (7.94%), 441.08 MWh (5.80%), 663.26 MWh (8.72%) and 1673.32 MWh (22.00%), respectively. In the plug load control scenarios, high-rise apartments made the largest contribution with a reduction of 134.15 MWh. Medium office buildings had the most reduction under the other three scenarios, with a reduction of 122.16 MWh for temperature reset, 107.90 MWh for lighting control, and 357.16 MWh for combination control. This study demonstrated a significant DR potential for buildings in Changsha to overcome the electricity shortage period. [ABSTRACT FROM AUTHOR]

• A solar assisted ground source heat pump system with graded thermal energy storage (SAGSHP-GTES) was proposed. • Performance for two SAGSHP systems were simulated and compared in depth. • The SAGSHP system showed a poor η SC of around 19.4%, while the SAGSHP-GTES system showed excellent performance, with a high η SC of around 42.7%, and the problem of soil temperature reduction was solved. • The SAGSHP-GTES system was a feasible and efficient technology for hot water supply in all climate zones of China. The solar assisted ground source heat pump system (SAGSHP) is recognized as an efficient, clean and economical renewable energy technology for hot water supply. However, in SAGSHP systems with an all-day hot water supply, the solar collectors can only heat the water tank with intense solar radiation, which wastes moderate and weak solar resources. In addition, as the SAGSHP system draws heat from the soil for a long time, the soil temperature around the borehole decreases, causing the performance of the GSHP to deteriorate. To solve the above problems, a SAGSHP system with graded thermal energy storage (SAGSHP-GTES) was proposed. This paper investigated the performance and feasibility of the SAGSHP-GTES system, using a case study of hot water supply in a campus dormitory in Changsha, China. Firstly, the drawbacks of solar energy utilization in the conventional SAGSHP system were analyzed. Then, the SAGSHP-GTES system was modeled by TRNSYS, and its operational performance was evaluated and compared with the conventional SAGSHP system. Finally, the feasibility and performance of the SAGSHP-GTES system in different climate zones of China were analyzed. The results showed that the conventional SAGSHP system had a poor solar energy utilization efficiency, with an average thermal efficiency of the SC (η SC) of only around 19.4%. The long period of operation caused the soil temperature to drop from 18.1 °C to 2.78 °C, which led to a decrease in system efficiency and an increase in electricity consumption. By controlling the heat collection of the SC and the graded utilization of solar energy in the SAGSHP-GTES system, a high η SC of around 42.7% was obtained, which improved the system efficiency while eliminating the problem of soil temperature drop. After 15 years of operation, the soil temperature increased from 18.1 °C to 19.9 °C, which led to an increase in the coefficient of performance of the system (COP sys) from 3.77 kW/kW to 3.84 kW/kW and COP GSHP from 3.45 kW/kW to 3.5 kW/kW, and a reduction in electricity consumption from 51,746 kWh to 50,735 kWh. In addition, the conventional SAGSHP system was not feasible in severe cold zones (Harbin) and cold zones (Beijing) of China, where soil temperatures fell below 0 °C and freezing of the circulating fluid occurred, while the SAGSHP-GTES system showed excellent performance in all climatic zones of China and is a feasible and efficient technology for hot water supply. [ABSTRACT FROM AUTHOR]