Abstract
A multi-objective wind farm integration framework is proposed in this paper which considers the composite generation and reliability assessment and annualized operating and investment cost evaluation. An emission-controlled policy is adopted such that the amount of SOx and NOx decreases in line with renewable resource planning. Since the incorporation of large-scale distant wind farms is a problem of the multi-objective mixed-integer type with nonlinearities and non-convexities, this paper utilizes a fast elicit multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA II) by probabilistic indices. It is noted that the impacts of the unavailability of the transmission system are modeled employing DC Optimal Power Flow (OPF) based on the incidence matrix together with the static security evaluation. Furthermore, in order to assess the performance of the suggested approach, the model is implemented on the Roy Billinton Test System (RBTS). Afterwards, distant wind farms integration into Iran's South-West Regional Grid (ISWRG) is studied. © 2019

Abstract
This paper presents an optimization framework to determine the optimal operating points of combined heat and power (CHP) units with nonlinear, nonconvex feasible operating region (FOR). The mentioned problem is the economic dispatch (ED) of heat-only units, thermal units, and CHP units. Also, the electric units are of thermal technology while their valve-point impact is taken into consideration. Also, the heat power curve of heat-only units is nonlinear. It is noted that the FOR of CHP units has been defined both as convex and nonconvex regions. For those with nonconvex characteristic, a method is proposed to convert it into a convex characteristic. Accordingly, a separate binary variable must be defined to determine the optimal operating point in each convex region. Thus, the presented problem in this paper is of mixed-integer nonlinear programming (MINLP) type modeled in General Algebraic Modeling System (GAMS) software. In this respect, different case studies have been presented to assess the optimality, feasibility, and the flexibility of the model. It is noteworthy that the valve-point effect of thermal units and different FORs of CHP units as well as the electrical power losses have been considered. © 2018 John Wiley & Sons, Ltd.

Abstract
The paper presents a multi-year, multi-objective framework for integrating Renewable Energy Sources (RESs) into the high voltage transmission network of Iran's National Power Grid (INPG). The objective functions in this study are the total cost, including the investment cost and operating cost for the planning horizon, and the system reliability. The first objective function is stated from the economic point of view, while the second objective function is considered as a security index in the expansion planning issue. The main purpose of this paper is to increase the RES penetration into the generation mix of INPG. Since the mentioned 230 to 400-kV INPG is a large-scale power system, the problem formulation is investigated in a mixed-integer programming, and then, the developed multi-objective problem has been solved using the augmented epsilon-constraint optimization method. In order to select the executive plan for installation, the fuzzy satisfying decision-making procedure is adopted in this study. © 2018 John Wiley & Sons, Ltd.

Abstract

Abstract
The interest in modeling the operation of large-scale battery energy storage systems (BESS) for analyzing power grid applications is rising. This is due to the increasing storage capacity installed in power systems for providing ancillary services and supporting nonprogrammable renewable energy sources (RES). BESS numerical models suitable for grid-connected applications must offer a trade-off, keeping a high accuracy even with limited computational effort. Moreover, they are asked to be viable in modeling for real-life equipment, and not just accurate in the simulation of the electrochemical section. The aim of this study is to develop a numerical model for the analysis of the grid-connected BESS operation; the main goal of the proposal is to have a test protocol based on standard equipment and just based on charge/discharge tests, i.e., a procedure viable for a BESS owner without theoretical skills in electrochemistry or lab procedures, and not requiring the ability to disassemble the BESS in order to test each individual component. The BESS model developed is characterized by an experimental campaign. The test procedure itself is framed in the context of this study and adopted for the experimental campaign on a commercial large-scale BESS. Once the model is characterized by the experimental parameters, it undergoes the verification and validation process by testing its accuracy in simulating the provision of frequency regulation. A case study is presented for the sake of presenting a potential application of the model. The procedure developed and validated is replicable in any other facility, due to the low complexity of the proposed experimental set. This could help stakeholders to accurately simulate several layouts of network services.

Abstract
Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.