Smart demand response in China: Challenges and drivers

To address the challenge of climate change, reducing emissions due to electric power generation and consumption has received increasing attention worldwide. The previous research efforts have been focused on emission reduction on the generation side, but the positive impacts through demand side load management are often ignored. This paper explores the models of load management to reduce cost and emissions. Five different load management methods are studied and compared using the IEEE 14-bus system and the IEEE 57-bus system. Two of them are centralized methods, i.e., temporal and/or spatial load management schemes that need to have global system information to optimize the overall load management in a whole control area. The remaining three are decentralized methods focusing on the customer side, including a basic self-optimizing method and two advanced self-optimizing methods [sliding window self-optimizing load management and day-ahead self-optimizing load management (DA-SOLM)]. The advanced SOLM schemes need multiple communications between an independent system operator and customers. The results show that the temporal and spatial load management can achieve an effective reduction in cost and emission and the DA-SOLM, especially for a large power system, is able to mitigate locational marginal price spikes.

Can a low-carbon-energy transition be sustained in post-Fukushima Japan? Assessing the varying impacts of exogenous shocks

Estimating electricity impact profiles for building characteristics using smart meter data and mixed models

At present, the electricity market reform has entered a deep area, electricity consumption forecasting has become increasingly important, accurate electricity consumption forecasting provides a reference basis and decision-making support for power dispatching and market transactions, and residential power consumption prediction can help users choose appropriate power suppliers and power supply programs according to the market situation, and provide the reliability and economy of power consumption. Residential electricity consumption is complex, affected by many factors and prone to significant noise disturbances, which results in electricity consumption data that is characterized by non-stationary, intermittent and erratic fluctuations. Therefore, using a single model is challenging to accurately predict household electricity consumption. To meet this challenge, this paper designs the structure of this three-step residential electricity consumption forecasting. At the first stage, we first analyzed cumulative effects of temperature on residential electricity consumption, and an hourly temperature correction model combining meteorological factors is constructed, the adjusted hourly temperature data are then entered into the predictive model. We have developed a Variable Modal Decomposition (VMD) data decomposition technique optimized for non-governmental organizational models, which improves the problem of subjectivity in parameter setting in traditional VMD, thus enhancing the performance and accuracy in data decomposition. In the second stage, developed a BiLSTM-AM based integrated deep learning model and dynamically adjusted the weights of the influencing factors by introducing an Attention Mechanism (AM) to enhance the stability of the model, also predict multiple IMF components obtained after the NGO-VMD decomposition, respectively. In the third stage, the training residuals of the BiLSTM-AM model are used as target variables to correct the prediction error in BiLSTM-AM using the XGBoost regression model. A variety of model configurations were constructed using actual data from a coastal province in southern China, and the computational results show that the integrated prediction model exhibits excellent stability and accuracy.

It analyses the impact of global climate change on electricity demand and its respective economic cost in buildings covering an area of 1 km by 1 km in the city of Madrid. In order to know the energy demand, meteorological information has been produced with a spatial resolution of 50 meters, taking into account the three-dimensional structure of the buildings and the land use properties around the buildings. Climate variables are dynamically downscaled from 1° to 50 m using a nesting approach. Energy simulations of buildings are implemented with the EnergyPlus model. To determine the cost of impacts, the future distribution of energy sources in the two climate scenarios analysed and the corresponding 2012 prices of the Spanish Energy Commission are taken into account. Impacts on the area’s energy demand are calculated for 2030, 2050 and 2100 versus 2011 under two IPCC global climate projections: RCP 4.5 (emission stabilization scenario) and RCP 8.5 (little effort to reduce emissions). The expected changes in electricity consumption in the year 2100 are very important. RCP 8.5 shows a strong increase in electricity demand for cooling buildings. In RCP 4.5 decreases in electricity consumption are observed (-14.37%) due to very important decreases in temperature. On average, the global climate for the year 2100 will have an impact on a typical building block in Madrid of 117918 euros per year according to scenario RCP 8.5 while in scenario RCP 4.5 110537 euros per year would be saved.

A framework for dynamic life cycle sustainability assessment and policy analysis of built environment through a system dynamics approach

Algorithms of forecast changes in the volume of electricity consumption from centralized and local sources as part of a combined power supply system are presented. The dependence of integrated accounting of hourly, daily and seasonal changes in electricity consumption on centralized and local energy supply systems has been developed. It is proposed to use an improved method of feasibility study of investment decisions at the stages of formalization of the technical task, based on the assessment of projected allowable costs for the construction of local power supply, which will allow consumers to have a positive economic effect and reduce the number of alternative solutions.

Country residential building stock electricity demand in future climate – Portuguese case study

With the further advancement of the new electricity marketization reform, in order to better solve the surplus water problem, it is urgent to combine with the current situation of China's electricity market reform to study the market mechanism and price mechanism to promote the consumption of hydropower. Under the background of the new round of electricity market reform, this paper firstly summarizes the current hydropower price mechanism in China, and discusses the influence of current price policies and methods on the development and consumption of hydropower based on the development status of hydropower. Secondly, according to the current situation and trend of China's electricity marketization reform, this paper discusses the types of market that hydropower can participate in at different stages of marketization, and studies the corresponding price mechanism that can promote the consumption of hydropower in different markets. Finally, the supporting safeguard measures are designed to coordinate with the price mechanism to ensure the consumption of hydropower.

More trading entities have participated in the electricity market owing to the electricity market reform. The pricing and settlement methods of the deviation power will affect the fairness of the transaction and the enthusiasm of the trading entities. The establishment of a reasonable deviation power settlement method is a key step to ensure the power market transaction. As an emerging distributed data storage technology and distributed accounting technology, blockchain technology has great application prospects in power market transactions and energy Internet. Therefore, the research on the application of blockchain technology to the settlement of deviation power not only contributes to the advancement of power market reform, but also has positive significance for the application of blockchain technology in the power field. In this paper, the concept of the deviation power, the measurement method and the existing settlement methods are briefly introduced. Then, the application of blockchain technology and the cross-regional energy trading based on this are analyzed. Finally, some transaction entities of China Southern Power Grid are taken as an example. The impact of different price and settlement ratios on the economic benefits and behavior of each transaction subject in terms of deviation power indicates how the blockchain technology can be applied to the settlement of deviation power in cross-regional power transactions.

This study investigates the mechanisms by which service-oriented transformation affects employee satisfaction with performance evaluation systems within the context of China’s electricity market reform. Using CGN New Energy’s Guangxi Branch as a case study and applying the grounded theory method, the research systematically analyzes employees’ perceptions of fairness, transparency, and career development during the transition from a product-oriented to a service-oriented model, based on in-depth interviews and surveys. The findings reveal that while servitization enhances employee skill development and career satisfaction, it also introduces challenges, such as increased work pressure and the demand for more transparent performance evaluation systems. This study provides valuable insights into optimizing management practices and performance evaluation systems in renewable energy companies, contributing both theoretically and practically to the literature on organizational transformation in the energy sector. These conclusions are not only significant for Chinese enterprises but also offer important reference points for global energy companies undergoing similar transitions.

Renewable energy policy and electricity market reforms in China

This chapter reviews the history of electricity market reforms and the evolution of electricity pricing mechanism and examines the effect of reforms on the improvement in efficiency of thermal power plants from 1998 to 2013. The results show that, during this period, significant improvement was achieved in the technical efficiency of thermal power plants calculated using the data envelopment analysis (DEA) method and in the efficiency of thermal power plants proxied by techno-economic indicators, including the auxiliary power ratio, the coal consumption rate, and the oil consumption rate. Two representative reform policies, the electricity system reform and direct trading policy, are considered to examine the effect of reforms on improvement in efficiency. It finds that the electricity system reform improves the efficiency of thermal power plants only in the short run and has minor influence on technical efficiency; the direct trading policy can reduce the oil consumption rate and the auxiliary power ratio of thermal power plants, and this effect is mitigated by increases in installed capacity. The government should thus continue to promote electricity market reform and emphasize the active role of market reform in power plant efficiency.KeywordsChinaEfficiencyElectricity sector reform

The increasing insistence on the environmentally friendly urban infrastructure requires intelligent structures that can take into account the heterogeneous sensor data, predictive analytics, and computational modeling. Conventional solutions can be ineffective in controlling the large-scale smart systems, leading to poor energy consumption and high carbon emission. In this paper, AI-SF2, a multisensor data fusion, AI-based predictive analytics, and computational modeling are proposed as an integrated framework to optimize smart infrastructures and green energy systems. Structural, environmental and energy sensor information are combined to offer strong situational awareness, and deep learning models predict energy demand, identify anomalies, and project carbon emissions. Real-time decision making and proactive resource allocation is simulated using computational models that provide dynamic interaction. Large scale simulations and testbed evaluations show up to 21.6 % energy savings, 18.9% renewable integration, and 24.2 % faster anomaly detection than current baselines. AI-SF2 provides a challenge-scalable, adaptive, and realistic solution to the sustainable smart cities. It also focuses on safe, privacy-capable sensor integration needed to have reliable smart infrastructure functioning.

Various conservation, load management, and fuel switching programs were considered as ways to reduce or shift system peak load. These programs operate at the end-use level, such as residential water heat. Figure D-1a shows what electricity consumption for water heat looks like on normal and extreme peak days. Load management programs, such as water heat control, are designed to reduce electricity consumption at the time of system peak. On the coldest day in average winter, system load peaks near 8:00 a.m. In a winter with extremely cold weather, electricity consumption increases fr all hours, and the system peak shifts to later in the morning. System load shapes in the Puget Sound area are shown in Figure D-1b for a normal winter peak day (February 2, 1988) and extreme peak day (February 3, 1989). Peak savings from any program are calculated to be the reduction in loads on the entire system at the hour of system peak. Peak savings for all programs are measured at 8:00 a.m. on a normal peak day and 9:00 a.m. on an extreme peak day. On extremely cold day, some water heat load shifts to much later in the morning, with less load available for shedding at the time of system peak. Models of hourly end-use consumption were constructed to simulate the impact of conservation, land management, and fuel switching programs on electricity consumption. Javelin, a time-series simulating package for personal computers, was chosen for the hourly analysis. Both a base case and a program case were simulated. 15 figs., 7 tabs.