Electric power supply and demand early warning based on PCA and SVM method

A limited review is presented of the outlook for the electric power supply and demand during the period 1980 to 1989. Only the adequacy and reliability aspects of bulk electric power supply in the contiguous US are considered. The economic, financial and environmental aspects of electric power system planning and the distribution of electricity (below the transmission level) are topics of prime importance, but they are outside the scope of this report.

Introduction The power grid controls their electric power supply by maintaining voltage, frequency, and transient stability, by predicting the electricity demand at any given time. However, recent large-scale implementation of photovoltaics (PV) makes it difficult to meet them because the gap between power supply and demand will be widening. The suppression of any power sources including solar power generation with sacrificing some electricity is sometimes adopted to avoid large-scale blackouts, and the situation is expected to be a common event at the 2030s. An effective solution to such situations is called as “leveling power”, which is the ability to maintain a balance between power supply and demand. Leveling power needs to be introduced not only into the centralized power grid but also into distributed energy systems. The major candidate of leveling power at distributed energy systems is electrochemical energy storage technology such as secondary batteries or hydrogen Power-to-Gas-to-Power (P2G2P). A smart energy system called Ene-swallow has been developed by our research group, and applied to Ookayama campus at the Tokyo Institute of Technology. This system manages 1400 kW-PV, 100 kW-FC, 70 kW gas engine and 100 kWh storage battery with a 10,000 kW-class electricity demand. Ene-Swallow also accumulates the power demand data in 1-sec intervals and PV power generation data in 1-min intervals. We previously carried out a techno-economic analysis of a hydrogen energy storage system to level the annual gap of power supply and demand [1]. Here we evaluated the potential of electrochemical energy storage as a leveling power with using the annual data from those data. Method We analyzed the current situation requirements for leveling power (analysis-1) and the applicability of a distributed energy storage system (analysis-2). For analysis-1, we analyzed 1-hour data of power generation and consumption published from power companies across Japan (Fig. 1a), and 1-min data of PV power generation (Fig. 1b) and 1-sec data of power demand (Fig. 1c) at Ookayama campus. For analysis-2, we set a tentative distributed grid with a 10,000 kW-class electricity demand, a given amount of primary power generation by PV and the power supply from the grid. In this work, we set no leveling power by the grid, which supplied constant power. Then, we determined the entire gap between power supply and demand during a single year based on the actual 1-minute data of PV power generation and power demand from April, 2017 to March, 2018. Then we estimated the required amount of energy storage, and thus determined the required number of P2G2P devices with alkaline electrolysis cell (AEC), solid oxide fuel cells (SOFC), H2 tanks and compressors, and lithium-ion batteries (LIB). In the system, the balance of power generation and consumption in a day is controlled as shown in Fig. 2. During the daytime, the surplus power from PV is leveled by an AEC. If AEC capacity is not sufficient, it is leveled by LIB. At night, the required power for demand is generated by an SOFC and/or an LIB. By optimizing the capacity of each component device in the distributed system, we evaluated the total system cost. We specified the expected technical parameters in the future (for the year 2030), such as unit price, efficiency and lifetime as shown in Table 1. Results From analysis-1, the 1-hour data showed that the supply fluctuation of PV power generation during the day strongly impacts the power grid when the energy mix ratio of PV to total annual demand reached about 10% when keeping nuclear power generation constant. The 1-minute data showed large fluctuations in PV generation within in a short time period (< 1 hr) during weather such as a cloudy day (Fig. 1b). Power fluctuations with different time scales have to be leveled by using electrochemical energy storage technologies. Analysis-2 showed that the total system cost could be reduced by using a combined system of P2G2P and LIB by utilizing their different characteristics. The combined system of P2G2P can reduce their total energy cost with dividing the role by power generation and the storage when the cost targets shown in Table 1 can be achieved. [Acknowledgements] A part of this study is supported by the New Energy and Industrial Technology Development Organization (NEDO) and MIRAI program of Japan Science and Technology Agency (JST). Reference [1] T.Okubo et al., 234th ECS Meeting AiMES 2018, (September 30-October 4, 2018) Figure 1

Assessment of Algeria's electricity power demands during COVID-19 pandemic and wildfires incidents

Electric vehicles such as trains must match their electric power supply and demand, such as by using a composite energy storage system composed of lithium batteries and supercapacitors. In this paper, a predictive control strategy based on a Markov model is proposed for a composite energy storage system in an urban rail train. The model predicts the state of the train and a dynamic programming algorithm is employed to solve the optimization problem in a forecast time domain. Real-time online control of power allocation in the composite energy storage system can be achieved. Using standard train operating conditions for simulation, we found that the proposed control strategy achieves a suitable match between power supply and demand when the train is running. Compared with traditional predictive control systems, energy efficiency 10.5% higher. This system provides good stability and robustness, satisfactory speed tracking performance and control comfort, and significant suppression of disturbances, making it feasible for practical applications.

As noted previously, at this writing about 88 percent of total energy consumed in the United States is provided by combustion of fossil fuels, An additional 7.5 percent is supplied from nuclear energy in the form of electric power. Electric power supplies about 40 percent of total energy demand and is the fastest growing form of energy use, the rate of use of electric power increasing about twice as fast as the overall rate of increase in energy consumption generally. With the exception of the period of dramatic reduction in energy use between 1978 and 1982, overall energy consumption in the United States over the past several decades has increased at a rate of about three percent per year, while demand for electric power has increased at a rate of about six percent per year.

Demand and supply-side determinants of electric power consumption and representative roadmaps to 100% renewable systems

The number of renewable power plants has recently been increasing; hence, it is essential to maintain a balance between the supply and demand of electric power. We employ the technique of demand response (DR) through the manipulation of air conditioners. In summer, the electric demand for air conditioning consumes the majority of the available total electric power; therefore, regulating this demand compliments the requirements of peak-cut and load leveling. However, the manipulation of air conditioning impacts the user comfort and the stability of the machine. Moreover, the energy consumption of air-conditioners is uncertain owing to the operation conditions. Consequently, it is difficult to estimate the performance of the DR method. In this study, we focus on the variable refrigerant flow system, which is a popular air-conditioning system in Japan. We evaluate the performance of the demand response method using a simulation analysis and ensure that the amount of DR to regulate the air-conditioner is controlled quantitatively.

India experiences a wide gorge between its electrical power demand and supply due to various factors like insufficient capacity, powers losses etc. Owing to this India serves as a perfect model to implement distributed generation wherein energy sources are located close to the end users by creating a microgrid with various sources feeding to the loads connected on the grid. A microgrid based on photovoltaic and fuel cell sources has been created using renewable energy sources like PV (Photovoltaic) and FC (Fuel cell) in Centre for Energy Studies (CES) at IIT Delhi. PVFC hybrid system not only produces power that is carbon free but also eliminates the environmental hazards caused by lead acid batteries. The present paper delineates the working of the microgrid and demonstrates the easy switching between power sources in order to support the load.

Energy balance between electrical power demand and supply should be always under control for a safe mission. It varies with many factors. Simulation is an important study to consider these factors and to perform a reliable analysis of power subsystem. TUGES (TUSAS Uydu Guc ve Enerji Simulatoru) is a tool which is developed by TAI, based on MATLAB/Simulink software. It has been developed to have a flexible and powerful tool to perform energy balance simulations for satellites.

Electricity demand is nowadays a very important development factor for growing economies. In order to support daily electrical demand and meet the customer daily needs, electricity and companies operators must rely on powerful and precise forecasting tools. PREVEOS-DZ was designed and implemented in order to predict Short and Midterm electrical energy and power demand in order to plan day to day production planning as well as yearly electrical development established by the Algerian Operator System. The software is based, on the multiple regression approach as well as Artificial Neural Networks, especially for power forecasting, offering models in both paradigms. The Forecast strategy is implemented not only for global national forecast but also for regional demands, even if the electrical grid is fully connected. PREVOS-DZ is implemented using C # and allows full compatibility with Excel tabular, used mostly by the System Operator. The results of PREVELOS-DZ obtained as a prevision exercise were validated on data from 2018 and presented in this paper.

In this study, a novel prediction method for electric power demand based on markov chain model with a fuzzy probability has been developed. The model improves upon the existing prediction methods with advantages in uncertainty reflection, such as the uncertainties in electric power system which reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to predict the electric power demand of a virtual city from 2011 to 2016. Different satisfaction degrees of fuzzy parameters are considered as different levels of detail of the statistic data. The results indicate that the model can reflect the high uncertainty of long term power demand, which could support the programming and management of power system.

Future Smart Grids will need to integrate thousands of independently operated intelligent energy devices into one organizational unit. Distributed ledger technologies can provide the means for a business-driven consensus between supply and demand of electrical power. This approach is limited by the computing power of the participating devices. We describe a way to increase the scalability of this approach by a) aggregating power consumption and generation, thus reducing the computational load on the blockchain and by b) applying the concept of edge computing to increase reliability and reaction time. We propose a hierarchical clustering approach for smart grid power balancing, which aims to advance our understanding of architectures for future real-world smart grids.

The impact of weather changes on the supply and demand of electric power and wholesale prices of electricity in Germany

Coordinated operation schedule of microgrids in consideration of penetration of variable renewable energy sources

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