This paper deals with the Optimal scheduling of a microgrid (MG) equipped with dispatchable distributed generators (DGs), renewable generators and electrical storages (batteries). A chance-constrained model is developed to handle normal operation and emergency conditions of MG including DG outage and unwanted islanding. Purchasing reserve from the upstream grid is also considered. Moreover, the uncertainties of loads and renewable resources are incorporated into the model. Furthermore, a novel probabilistic formulation is presented to determine the amount of required reserve in different conditions of MG by introducing separate probability distribution functions (PDFs) for each condition. Accordingly, an index named as the probability of reserve sufficiency (PRS) is introduced. The presented model keeps a given value of PRS in normal and emergency conditions of MG operation. In addition, some controllable variables are added to the chance constraints as an innovative technique to reduce the complexity of the model. Finally, a test microgrid is studied in different case studies and the results are evaluated.

In this paper, various types of stochastic dynamic programming models (SDP) and also a deterministic dynamic programming (DP) are presented and compared for multipurpose Dez reservoir dam located in southwest of Iran. The impact of inflow forecasting uncertainty in optimum reservoir operation is investigated through two types of stochastic dynamic programming models. SDP models are different based on hydrologic state variable and inflow conditional or non conditional probability assumptions. A simulation model is developed to investigate the achieved optimum policies in different models. So, average of different operation variables and also performance criteria such as reliability, resiliency and vulnerability are used to compare the results. In two SDP models which apply current inflow instead of previous time step inflow as hydrologic state variable, real time simulation is done with forecasted and observed inflows. The effect of forecasting accuracy and different forecasting methods in reservoir operation are also studied. In general, objective function is considered as minimizing the sum squared of two sided deviations from target release and storage. This research shows that lack of attention to inflow forecasting in models which need it and assuming known values could lead us to unreal and false results and mislead us in selecting type of model. On the other hand, it is noticed that forecasting accuracy plays an important role in optimum reservoir operation.

In this paper, the problem of Optimal stochastic operation of thermal units, combined heat and power (CHP) units, wind turbines (WTs) and photovoltaic (PV) units with the objective of reducing total operating costs is defined as an optimization problem. In this problem, the uncertainties in load, photovoltaic and wind units are considered in terms of solving the two-point estimation method. The amount of power and heat output in different units, the position of the transformers tap and the reactive power injection by the parallel compensators are the control variables of the optimization problem. A particle swarm optimization algorithm with comprehensive learning is used to solve this optimization problem. In order to evaluate the performance of the proposed method, the IEEE 118-bus standard test system consisting of thermal, CHP, WT and PV units is selected. The simulation results in MATLAB software environment show the superiority of the comprehensive learning particle swarm optimization method compared to other optimization methods.

Energy hub (EH) concept is widely proposed for integrating different types of energy infrastructures. EH physically consists of some storage systems and converters receiving energy from multiple sources immediately from its upper grids and provides energy services for ultimate consumers. In this paper a state space model for EH system is proposed. Due to the dynamic behavior loads and the price uncertainties, a Model Predictive Control approach is suggested for Optimal performance. The proposed method is studied on a EH that consists of transformer, boiler, CHP, electrical and heat storages considering demand side management. Finally, the simulation results depicts to demonstrate the effectiveness of the proposed method for Optimal operation of the EH.

This study presents an Optimal framework for the operation of integrated energy systems using demand response programs. The main goal of integrated energy systems is to Optimally supply various demands using different energy carriers such as electricity, heating, and cooling. Considering the power market price, this work investigates the effects of multiple energy storage devices and demand response programs, including the time of use pricing, real-time pricing, and integrated demand response on Optimal operation of energy hub. Moreover, impacts of different optimization methods are evaluated on the Optimal scheduling of multi-carrier energy systems. Maximizing profits of selling electrical energy and minimizing the purchasing cost of input carrier energies are considered as objective functions to indicate bidirectional interchanges of energy hub systems with the power grid. To minimize the generation cost of energy carriers, a new quadratic objective function is also optimized using genetic algorithm. In this study, Optimal operation of the energy hub based on the proposed quadratic objective function is an economic dispatch problem where the purchasing electrical power by the energy hub is considered as a load of the upstream grid. The optimization problem is implemented in the sample energy hub to indicate the effectiveness of different energy storage roles and applied demand response programs in the Optimal operation of energy hub systems.

This paper proposes a novel energy hub model for areas using both heat and cold demands that arise due to the major changesin environmental temperature in different periods of the year. The energy demand and the electrical price in a competitiveelectricity market are uncertain with stochastic values which are usually performed by a probability distribution function. Therefore, a stochastic mathematical model representing an Optimal operation of energy hub is based on the objective functionof minimization of energy costs (including electricity and gas). Several constraints such as energy balance, limited capacityof the transformer, air conditioners, gas boilers, absorption chillers, combined heat, and power and battery energy storagesystem are also incorporated into the model to guarantee the required specifications. The high-level algebraic modelingsoftware, general algebraic modeling system has been employed to undertake calculations. Finally, numerical results haveillustrated the efficiency and capability of the proposed models.

Under water shortage conditions, it is necessary to exercise water consumption management practices in water distribution networks (WDN). Intermittent supply of water is one such practice that makes it possible to supply consumption nodal demands with the required pressure via water cutoff to some consumers during certain hours of the day. One of the most important issues that must be observed in this management practice is the equitable and uniform water distribution among the consumers. In the present study, uniformity in water distribution and minimum supply of water to all consumers are defined as justice and equity, respectively. Also, an optimization model has been developed to find an Optimal intermittent supply schedule that ensures maximum number of demand nodes are supplied with water while the constraints on the operation of water distribution networks are also observed. To show the efficiency of the proposed model, it has been used in the Two-Loop distribution network under several different scenarios of water shortage. The optimization model has been solved using the honey bee mating optimization algorithm (HBMO) linked to the hydraulic simulator EPANET. The results obtained confirm the efficiency of the proposed model in achieving an Optimal intermittent supply schedule. Moreover, the model is found capable of distributing the available water in an equitable and just manner among all the consumers even under severe water shoratges.

Optimal operation of local resources can be effected in conservation water resources of paddy fields and corrected water distribution. Therefore, in this study, a simulation-optimization model of exploit ponds has been developed for investigate the role of ponds to store local water and use it to supply irrigation water requirement of paddy fields. Evapotranspiration, irrigation water requirement, irrigation interval and pond balance were simulated and then number of canal intake, storage volume and released volume of pond were optimized by binary genetic algorithm. The developed model was applied for Fashtam pond in Sangar, Gilan province. The result showed that in normal year 54% of irrigation requirement of paddy fields were supplied by rainfall and runoff water saved in Fashtam pond and the rate of over flow water of pond to maximum volume of that were 74%. The model result for scenario of increasing pond storage two times showed the rate of pond in supplying irrigation requirement of paddy fields is increased to 91%.