Eco-efficiency considering the issue of heterogeneity among power plants

One of the main objectives in restructuring power industry is enhancing the efficiency of power facilities. However, power generation industry, which plays a key role in the power industry, has a noticeable share in emission amongst all other emission-generating sectors. In this study, we have developed some new Data Envelopment Analysis models to find efficient power plants based on less fuel consumption, combusting less polluting fuel types, and incorporating emission factors in order to measure the ecological efficiency trend. We then applied these models to measuring eco-efficiency during an eight-year period of power industry restructuring in Iran. Results reveal that there has been a significant improvement in eco-efficiency, cost efficiency and allocative efficiency of the power plants during the restructuring period. It is also shown that despite the hydro power plants look eco-efficient; the combined cycle ones have been more allocative efficient than the other power generation technologies used in Iran

Factors Associated with The Incidence of Coronary Heart Disease in The Mashad: A Cohort Study

Coronary heart disease (CHD) is the leading cause of morbidity and mortality globally, and specifically in Iran. Accurate assessments of Coronary heart disease (CHD) incidence is very necessary for public health. In current study we aimed to investigate the incidence of CHD and importance of several classical, modifiable and un-modifiable risk factors for CHD among an urban population in eastern Iran after 6 years of follow-up. Methods The population of MASHAD cohort study were followed up for 6 years, every 3 years in two step by phone and who reported symptoms of CVD were asked to attend for a cardiac examination, to estimate the incidence of CHD with 95% confidence interval (95% CI) as well multiple logistic regression analysis was performed to assess the association of several baseline characteristics with incidence of CHD event. Evaluation of goodness-of-fit was done using ROC analysis. CHD cases divided into four different classes which include: stable angina, unstable angina pectoris, myocardial infarction and sudden cardiac death. Results In the six years\u27 follow-up of Mashhad study, the incidence rate of all CHD event in men and women in 100,000 people-years with 95% confidence intervals were 1920 (810-3030) and 1160 (730-1590), respectively. The areas under ROC curve (AUC), based on multivariate predictors of CHD outcome, was 0.7825. Conclusion Our findings indicated that the incidence rate of coronary heart diseases in MASHAD cohort study increases with age as well as our final model designed, was able to predict approximately 78% of CHD events in Iranian population

A smart charging algorithm for integration of EVs in providing primary reserve as manageable demand-side resources

Summary In this paper an efficient algorithm for simultaneous scheduling of energy and primary reserve with the presence of smart electric vehicles in the system is discussed. It is proposed that the system operator uses the electric vehicles (EVs) as alternative resources of primary reserve in the power system. The proposed model deals with the effect of generation scheduling on the EV charging schedules and primary reserve capacities. In fact, the amount of primary reserve provided by EVs is highly related to the EV's charging schedules. Therefore, in this paper a smart charging algorithm is also proposed that is based on direct load control of EVs by the system operator. The proposed scheme would be applicable with the lowest intelligence level and will not impose extra investment cost to the EV owners. All the nonlinear constraints included in the primary reserve scheduling are linearized to be solvable by mixed integer linear programming method. A case study on IEEE RTS79 system with 30% EV penetration is used to illustrate the feasibility and acceptable performance of the proposed method. The influence of EVs' participation on operation costs, load curve, and EV bills is discussed

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section

Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market

Many real world problems have big data, including recorded fields and/or attributes. In such cases, data mining requires dimension reduction techniques because there are serious challenges facing conventional clustering methods in dealing with big data. The subspace selection method is one of the most important dimension reduction techniques. In such methods, a selected set of subspaces is substituted for the general dataset of the problem and clustering is done using this set. This article introduces the Shared Subscribe Hyper Simulation Optimization (SUBHSO) algorithm to introduce the optimized cluster centres to a set of subspaces. SUBHSO uses an optimization loop for modifying and optimizing the coordinates of the cluster centres with the particle swarm optimization (PSO) and the fitness function calculation using the Monte Carlo simulation. The case study on the big data of Iran electricity market (IEM) has shown the improvement of the defined fitness function, which represents the cluster cohesion and separation relative to other dimension reduction algorithms

Optimal Scheduling of a Hydrogen-Based Energy Hub Considering a Stochastic Multi-Attribute Decision-Making Approach

Nowadays, the integration of multi-energy carriers is one of the most critical matters in smart energy systems with the aim of meeting sustainable energy development indicators. Hydrogen is referred to as one of the main energy carriers in the future energy industry, but its integration into the energy system faces different open challenges which have not yet been comprehensively studied. In this paper, a novel day-ahead scheduling is presented to reach the optimal operation of a hydrogen-based energy hub, based on a stochastic multi-attribute decision-making approach. In this way, the energy hub model is first developed by providing a detailed model of Power-to-Hydrogen (P2H) facilities. Then, a new multi-objective problem is given by considering the prosumer’s role in the proposed energy hub model as well as the integrated demand response program (IDRP). The proposed model introduces a comprehensive approach from the analysis of the historical data to the final decision-making with the aim of minimizing the system operation cost and carbon emission. Moreover, to deal with system uncertainty, the scenario-based method is applied to model the renewable energy resources fluctuation. The proposed problem is defined as mixed-integer non-linear programming (MINLP), and to solve this problem, a simple augmented e-constrained (SAUGMECON) method is employed. Finally, the simulation of the proposed model is performed on a case study and the obtained results show the effectiveness and benefits of the proposed scheme

A New Approach for the Incorporation of the End-User’s Smart Power–Electronic Interface in Voltage Support Application

Technology advancement in power–electronic interfaces and their evolution open an opportunity to end-users to benefit from their newfound capability. For the end-users, power–electronic interfaces can act as Distributed Energy Resources (DERs) for reactive power injection and absorption. If these power–electronic interface capabilities can be properly integrated into traditional utility system operations, they can be used as beneficial tools for distribution management and voltage profile enhancement. Considering the present distribution system, it is not possible to communicate to all DERs. In this paper, we considered two proposed residential-control and droop-control methods. The multi-criteria decision-making technique (MCDM), along with fuzzy theory, was used to prioritize candidate buses for their participation in the Volt-VAR program. In this paper, the contribution of active DERs in reactive power compensation was evaluated