Abstract

Abstract
The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation's reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT), a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV), wind turbine (WT), fuel cell (FC), diesel engine (DE), MT and energy storage (ES). Four typical scenarios were designed according to different day types (work day or weekend) and weather conditions (sunny or rainy) in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers' comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO) to propose modified chaos particle swarm optimization (MCPSO) whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

Abstract

Abstract
As a recently increasing trend among different applications of smart grid vision, smart households as a new implementation area of demand response (DR) strategies have drawn more attention both in research and in engineering practice. On the other hand, optimum sizing of renewable energy based small scale hybrid systems is also a topic that is widely covered by the existing literature. In this study, the sizing of additional distributed generation (DG) and energy storage systems (ESSs) to be applied in smart households, that due to DR activities have a different daily demand profile compared with normal household profiles, is investigated. To the best knowledge of the authors this is the first attempt in the literature to investigate this issue, also including step-wise decreasing cost functions for DC and ESS, varying load and DG production profiles seasonally, and weekday-weekend horizons for a long-term analysis period. The study is conducted using a mixed-integer linear programming (MILP) framework for home energy management system (HEM) modeling and techno-economical sizing. Also, different sensitivity analyses considering the impacts of variation of economic inputs on the provided model are realized.

Abstract
Single-stage grid connected inverters are considered as an economic, compact and simple topology compared with multi-stage inverters. In photovoltaic (PV) grid connected systems, the major requirement is to achieve maximum output power from the source. Maximum Power Point Tracking (MPPT) techniques require measurements on the DC side of the inverter connected to the PV in order to determine the current operating point on the power characteristics. Typically this is achieved by perturbing the reference output power and observe the change in the PV voltage, current or both. Based on the observation, it could be determined whether the current operating point is beyond or below maximum power. This paper presents an MPPT technique for a single-stage PV grid connected inverter where the MPPT algorithm determines the current operating point at different operating conditions based upon observing the inverter controller action. Such approach eliminates the requirement of sensing elements to be added to the converter which aids the advantages of the single-stage converter. Design of the utilized PV system is derived based on filter parameters, PV panel selection and controller parameters. Using simulation and practical implementation, the performance of the proposed MPPT technique is evaluated for the PV grid connected system.

Abstract
This work presents a new integrated multistage and stochastic mathematical model, which is developed to support the decision-making process related to the expansion planning of distribution network systems for integrating large-scale distributed "clean" energy sources. The developed model, formulated from the distribution system operator's point of view, determines the optimal sizing, time, and placement of distributed energy technologies (renewables, in particular) as well as that of energy storage systems (ESSs) and compensators in distribution networks. The ultimate goal of this optimization work is to maximize the size of distributed generation (DG) power absorbed by the system while maintaining the power quality and stability at the required/standard levels at a minimal cost possible. The model, formulated as a mixed-integer linear programming optimization, employs a linearized alternating current network model that captures well the inherent characteristics of power network systems, and balances accuracy with computational burden. The standard IEEE 41-bus distribution system is used to test the developed model and carry out the required analysis from the standpoint of the objectives set.The results of the case study show that the integration of ESS and compensators helps to significantly increase the size of variable generation (wind and solar) in the system. For the case study, a total of 10. MW demand wind and solar power has been added to the system. One can put this into perspective with the peak load 4.635. MW in the system. This means it has been possible to integrate renewable energy source (RES) power more than twice the peak demand in the base case. It has been demonstrated that the joint planning of DGs, compensators, and ESSs, proposed in this work, bring about significant improvements to the system, such as reduction of losses, cost of electricity and emissions, voltage support, and many more.The expansion planning model proposed here can be considered a major leap forward toward developing controllable grids, which support large-scale integration of RESs (as opposed to the conventional "fit and forget" approach). It can also be a handy tool to speed up the integration of more RESs until smart-grids are materialized in the future.