The grids are facing the critical issue concerning the power imbalance. Demand response (DR) program are increasingly promoted to encourage the end-users to change their load profiles under a specified pricing policy or request of the grid. For buildings, the supply-based fast demand response strategy has been demonstrated that it can quickly response to the urgent request from smart grid and can reduce the load demand within very short interval. However, flow sensors are required for each air conditioning terminal equipment. In view of the high cost of flow sensors and the large number of airconditioning terminal equipment, there are few cases of installing flow sensors for air-conditioning terminal equipment in existing large public buildings. Therefore, it hinders the application of this method in practical projects. This study aims at developing an improved fast demand response strategy of building HVAC system with low cost measurement sensors for smart grid applications. The virtual flow meter was modelled to estimate the water flow rate of each AHU based on theair side measurements. Meanwhile, a modified selfadjustment chilled water distribution method was developed to realize the balanced distribution of cooling capacity in different indoor zones, which does not require additional work to offline identify the key parameters prior to application.
Based on the high-potential 5G network and ubiquitous power Internet of things, the concept of smart energy has been proposed to solve the problem of optimal utilization of the power system’s generation, transmission, distribution, consumption, and the related services. Therefore, innovation and reformation will be doomed and embraced by smart energy in China’s energy system, which includes technological progress and system mechanism reform. In the meanwhile, new solutions and challenges would be provided by smart energy for the State Grid Corporation of China’s business model. Focused on the analysis of smart energy marketing strategy and profit model, this paper lists the business model and scenarios under the developing tendency of smart energy and provides a detailed sorting and analysis for the smart operation and management of user service and ecological platform. This article aims to analyze the value of smart energy and provides possible business model choices for integrated energy suppliers.
In order to address the various challenges and well utilize the opportunities brought by the increasing penetration of distributed energy resources at the demand side of power systems, a new paradigm, peerto-peer (P2P) energy trading, has emerged in recent years, where prosumers and consumers are able to directly trade energy with each other. Besides the inherent potential benefits such as facilitating local power and energy balancing, a P2P energy trading community as a whole also has the potential to provide ancillary services to power systems to create additional value. In this paper, a price-based mechanism was proposed, in which the customers of a P2P energy trading community can further respond to the price signals issued by power utilities to provide ancillary services such as demand reduction and generation curtailment. A continuous double auction with a residual balancing mechanism was proposed as the P2P energy trading mechanism. Simulation results verify that the proposed mechanisms are able to increase the social welfare of the whole P2P energy trading community without compromising any individual’s interests, and at the same time incentivize customers to provide ancillary services to power utilities.
The objective of this paper is to study and make a comparison between an original boosted-system with one Booster (B-MED) and an optimized system with two boosters (2B-MED) of a combined trans-critical CO2 refrigeration and boosted multi effect desalination system. The two systems are analyzed and compared thermodynamically. The optimized system with two boosters produces around 361.72 m3/day of fresh water, on the other hand the original system with only one booster module produces around 290.3 m3/day of fresh water, which means that the optimized system with two boosters increased the fresh water production rate by 24.6 % in comparison with that of one booster module. In addition, the heat transfer rate of the gascooler to the environment in the original system is equal to 1059 kw while it is equal to 472.5 kw in the optimized one, which means that the optimized system (2B-MED) improves the refrigeration system by decreasing the heat transfer rate of the gas-cooler by 55.38 %. This leads to the reduction of the heat transfer area (HTA) of the gas cooler and all that will lead to the decrease of the total annual cost (TAC) of the refrigeration system. So that, the optimized system with two boosters is thermodynamically better than the original one.
This paper presents a household battery charging and discharging game for a power supply-demand regulation in a peer-to-peer energy sharing, operating in the day-ahead electricity market. The problem is formulated as a noncooperative Nash equilibrium game where the households are considered selfish but rational players whose objectives are to optimize their individual battery state of charge and energy cost. The application of the proposed model to a practical case study of three households shows the potential of the households to regulate the electricity in the smart grid and save their energy costs. Households 1, 2 and 3 operating in the proposed model saved energy costs of up to 59.8%, 58.8% and 58.9%, respectively compared to them operating in a strictly real-time electricity market and household 1, 2 and 3 also had savings of up to 10%, 3.8% and 8.4%, respectively compared to them operating in a strictly day-ahead electricity market.
A new online fault diagnostic method for photo voltaic array is proposed in this paper, which is based on the Extreme Gradient Boosting (XGBoost)classifier. Firstly, the string current, array voltage, temperature and irradiance are measured by a monitoring system, from which a seven-dimensional fault feature vector is extracted as the input of the fault diagnosis model. Secondly, based on the XGBoost classifier, a new fault diagnosis model is established. Lastly, the feasibility and superiority of the proposed XGBoost based fault diagnosis model are tested by both Simulink based simulation and real fault experiments on a laboratory PV system. The correct rate of fault diagnosis in Simulink simulation is 99.99%, while the correct rate of fault diagnosis in laboratory PV power plant simulation is over 99.90%. Extreme learning machines (ELM) and Random Forests (RF) are tested for comparison. Experimental results demonstrate the superiority of the proposed XGBoost based model.
In this paper, a novel rapid modeling method is proposed for solar photo voltaic (PV) modules, which is based on extreme learning machine and current-voltage (I-V) characteristic curves. Firstly, original I-V curves are down sampled to reduce data redundancy, and a simple method is proposed to detect and remove abnormal I-V curves. Secondly, a single hidden layer feed forward neural network is proposed as the model, which is then trained by the extreme learning machine (ELM) algorithm. Finally, the proposed ELM based method is tested using a large data set of experimental I-V curves provided by the National Renewable Energy Laboratory (NREL). Experimental results show that the proposed ELM based method can shorten the modeling time to 0.2~0.4s, and the root mean square error (RMSE) can reach 0.0484%~0.374%. Compared with other conventional artificial neural network based methods,the proposed method can greatly shorten the modeling time and significantly improve the accuracy and the generalization performance of the modeling for PV modules.
Combined cooling, heating and power (CCHP) is getting more attention for its energy saving. There has been a large number of researchers focus on the system configuration and operating strategies. However, the analysis of the whole process of the system from planning to design and running is relatively few. This paper based on an actual project in China, which has been running for two winters. Based on the background, planning, design and operating of the project, this paper comparing the load results between design and running stages, and it is found that the cooling and heating load in the running time is much smaller than the designed load. But the performance of CCHP is indeed better than the other conventional systems in the same building load situation. Finally, this paper proposed some suggestions for the future CCHP projects in the different stages.
The energy consumption and CO2 emission of China’s passenger transport have been increasing in recent years. With China’s population, economic development level, passenger volume, public transportation share, private car stock, and new energy vehicle (NEV) policies developing year by year, we need a medium – and longterm model to predict the future energy demand and greenhouse gas emissions of China’s passenger transport. In this paper, we divide the passenger transport sector into inter-city and inner-city, then establish a bottom-up model using the LEAP (long-term energy planning system) platform to estimate China’s provincial passenger transport emissions up to 2050. Four scenarios, namely reference (REF), business as usual (BAU), electric vehicles promoting(EVP) are set to evaluate possible policy alternatives. The results show that the BAU scenario and EVP scenario are efficiently reduce the energy consumption and CO2 emissions. Under the BAU scenario and the EVP scenario will reduce 45% and 53% energy consumption respectively. Under the BAU scenario and the EVP scenario will reduce 78% and 91% CO2 emissions respectively. The results show that promoting the development of electric vehicles will help China to achieve the goal of low-carbon transportation.
Carbon trading markets play an important role in emissions mitigation through financial tools. China has established seven carbon trading pilots in its major cities and provinces. This paper explores the evolution laws of the co-movement of daily prices between carbon markets using complex network theory. First, we combine the co-movement of prices in five continuous days to co-movement modes. Then, we construct a directed weighted complex network. The nodes are the co-movement modes. Edges are defined as the time adjacent relations of two nodes. The frequency of an edge is taken as its weight. Transaction prices for the pilots in Hubei and Shanghai are selected as the samples. Results show an appearance of 231 modes from the 243 possible patterns, indicating a scattering of co-movement modes. Among all modes, the most frequent one is the fully stable one, showing that the markets are inactive in most time. Compared to the full sample and other periods, the complex networks in the first sample period stands out due to its large nodes and the existence of rings. This finding indicates the exact mirroring of some successive co-movement modes. The method proposed in this paper helps in understanding the evolution of Intermarket co-movement.