Journal of Energy Management and Technology
Volume:1 Issue: 1, Spring 2017

Energy Hub is an appropriate framework for modeling and optimal scheduling of multi-energy systems (MES). However, the optimal scheduling problem in the energy hub models is fed by various technical, economic, social and environmental parameters. Many of these parameters are mainly subject to uncertainties. Fluctuating nature of renewable energy sources (RES), energy prices in deregulated markets, the behavior of consumers, simplifications and approximations in modeling, linguistic terms of experts, etc. are just a few examples of uncertainty sources in the optimal scheduling problem of energy hub. Ignoring such uncertainties in the process of modeling and optimization of energy hub results in unrealistic models and inaccurate results. On the other hand addressing these uncertainties leads to increased complexity of modeling and optimization. Therefore, identifying appropriate methods to address uncertainties is essential to achieve a realistic model of MES in the framework of energy hub. This paper reviews the different uncertainty modeling methods in optimal scheduling of energy hub. In this paper, different modeling and optimization methods of energy hub are discussed and strengths and weaknesses of these methods are demonstrated. A classification and review of the various uncertainty modeling methods implemented in the most recent research on MES are done to identify efficient methods for using in energy hub models. This paper concludes that future energy hub models need to realistic scheduling and modeling of MES to be able to achieve a realistic and comprehensive model of future sustainable energy systems.

Electricity consumption increases continuously because of several reasons such as population growth. Due to consumption growth of electricity it is necessary to upgrade generation, transmission and distribution equipments. In distribution level, transformers of sub-transmission substations should be upgraded to overcome load growth. In this paper, it is recommended to use wind generators and storage devices instead of transformer upgrading of substations. This is significant because of environment pollution and investment deferral of sub-transmission substations. Hence, this paper suggests a new method to determine the optimal capacity of wind generators and energy storage system (ESS) for investment deferral of sub-transmission substations. In this method reliability and economic aspects are considered along with time varying loads. Furthermore, to peruse the uncertainty of wind generation an innovative point estimate method (PEM) has been fulfilled. The main goal of the presented method is to minimize the investment cost of the ESS units and wind generators, purchased power from upstream network and also reliability maximization. The objective function is mathematically formulated as a mixed-integer nonlinear programming problem and subsequently solved by genetic algorithm (GA). The proposed method is successfully applied to a study case and obtained results show the efficiency and applicability of the proposed approach.

his paper shows an application of a scenario-based method for risk constrained stochastic optimal power flow (RC-SOPF) problem in electricity utilities. A two-stage stochastic programming framework is developed for dealing with various uncertainties. Customers' demand, wind power generation, and electricity price are considered as the uncertain parameters in the proposed RC-SOPF problem. The aim is to minimize the energy procurement costs, while preserving an acceptable risk level. The energy procurement cost consists of generators active power generation costs, cost of energy procurement from external network (e.g. pool market or upstream network) and operation & maintenance cost of wind farms. To control the negative impacts of the uncertainties, variance and conditional value at risk (CVAR) are used as risk measures. The proposed model is implemented on the 39-bus New England test system. The obtained results show that CVAR is suitable index for management of the risk associated with uncertain parameters in comparison with variance.

This paper presents a new scenario based method to prevent voltage instability under wind and load uncertainties considering correlation among wind turbines and loads. The correlated load and wind scenarios are generated based on the correlation matrix as well as Normal and Rayleigh probability density functions. Electrical distances are used to generate the correlation matrix among loads. Then, the preventive voltage instability problem is formulated two-stage stochastic programming problem. Control facilities include rescheduled active and reactive power of generation units, load shedding and demand response. The considered control facilities are classified into two different categories based on the stage of decision making. These categories are named here-and-now and wait-and-see. Demand response, load shedding and reactive power output of power plants are wait-and-see facilities, whereas active power of power plants is considered as here-and-now facility. The proposed method is tested on the standard IEEE 118-bus test system. Comprehensive analyses are carried out demonstrating the impact of uncertainties and correlations, as realistic load and wind modeling, on the problem

Fuel cell energy systems can deliver green energy efficiently without CO2 emissions which are nominated as a substitution choice for the conventional power sources. This paper proposes a new high step-up DC-DC converter which is applicable in distributed energy resources, especially for fuel cell power conditioning systems. The converter contains a bidirectional boost cell with a coupled inductor, two transformers whose secondary sides are connected in series to increase the voltage gain and also a voltage doubler. Leakage inductance of coupled inductor limits the currents of output diodes and results in Zero Current Switching (ZCS) of the diodes. The converter schematic is presented at first and then its analysis results in different modes of operation are given. After that the theoretical formulation of voltage gain are achieved and proved. Finally, simulations results for a 400 W/ 380 V load are presented to validate the claimed characteristics of the proposed converter.

In this paper, a multi-objective optimization model is proposed to calculate best possible size of energy storage system (ESS). The proposed model is solved utilizing weighted sum method. Positive effects of demand response program (DRP) are considered in the proposed paper. By utilizing the weighted sum method, many various solutions are obtained. Then to select the best possible solution, fuzzy satisfying approach is employed. The proposed multi-objective model includes two conflicting objective functions: 1) the first objective function is minimization of microgrid investment cost as well as operation cost; 2) the second objective function is minimization of loss of load expectation (LOLE). Microgrid includes some local units inside itself which may have some unknown outages and also due to variable and unstable output of renewable units, utilization of ESS is essential to improve stability of microgrid. Impact of DRP implementation is evaluated on microgrid related costs and the results are compared to validate the proposed technique. In order to simulate and model the proposed stochastic ESS optimal sizing problem in a microgrid, a mixed-integer program (MIP) is utilized.

Wind energy is the most economical and clean energy, which has considerably developed in recent years.Wind turbines can be classified by different indicators. One of the main classifications is based on the drive train and generator technologies. This study is based on a multi-parameter survey to develop an optimal decision-making algorithm for the technology selection. Two key technologies, including Permanent Magnet Generator (PMG) turbines and geared Doubly Fed Induction Generator (DFIG) turbines, are considered to be suitable options according to turbine and sites’ technical, economic and geographical parameters. Economic indices, such as IRR and NPV of a wind farm, are reported for each technology used in the model.A 50MW wind farm in Iran has been modeled in this article as a case study. Results show that according to Iran’s financial and economic fluctuations, a DFIG turbine, with 43% IRR and 52 M€ NPV is the most efficient technology for Iran. Specifically, its low initial investment and high efficiency and 48% capacity factor makes this turbine as the most suitable technology for Iran. Results show that 3.9 and 4.2 years payback period for DFIG and PMG wind turbines respectively, therefore the PMG turbines make a longer payback period for the wind farm.

This paper presents an adaptive multi-agent based online-tuned PID controller using Neuro-Fuzzy (NF) for dynamic management of Distributed Generations (DGs) in an autonomous microgrid. Increasing system stability and decreasing generation costs are the main aims of the proposed management strategy. Instead of one centralized management systems, the management and control function is allocated to several autonomous units which are known as agents. The proposed management system is composed of fixed and variable units. The fixed variables is the three parameters ( and ) of the conventional PID controller which are adjusted based on load variation pattern in offline mode. The parameters of variable unit is generated by NF system. The load pattern is applied to system in offline mode and agent’s optimizing units optimize the system performance. Distributed multi-agent model is considered for tuning the neuro-fuzzy parameters whereas agents establish with neighboring agents. In autonomous mode of the microgrid, the variable units, after tuning, control the system frequency and manage energy generation of DGs, beside fixed units, in an online manner. In the study system, various kinds of DGs including wind turbine, photovoltaic, synchronous generator, and fuel cell are considered. Linear transfer function models are obtained for each DG unit. To achieve a better performance of the proposed management strategy the modified Particle Swarm Optimization algorithm is applied for tuning of the NF based PID controller parameters. Simulation results in various conditions of microgrid confirm the good performance of the proposed management strategy in comparison to the other methods.