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With the growing trend of emerging new technologies in distribution networks, such as wind turbines, solar panels, electric vehicles, and distributed generations, the passive distribution systems may become ‘active’ which requires more study in the area of integrated transmission and distribution systems (ITDSs) and corresponding bilateral interactions. To solve this problem, most of the studies connect distinct simulators to create a novel co-simulation framework for ITDS. In this study, the authors present a literature survey of existing ITDS co-simulation frameworks along co-optimisation in ITDS. These frameworks are categorised on multiple characteristics, such as simulation tools, synchronisation methods, and research topics. Furthermore, they propose a software platform that is focused on the integrated generation, transmission, distribution, and customer systems (IGTDCSs). The proposed framework also comprises several technological dimensions such as stochastic optimisation, high-performance computing, and high-level design software architecture for planning integrated and flexible power networks and optimising their technological trajectories and operational functioning considering uncertainties. By developing a prototype informed with software engineering and complex system design approaches, they will demonstrate the relevance of a unified vision of IGTDCS simulation in a minute-by-minute horizon, a vision that may later benefit electromagnetic transient simulation or stability co-simulation tools, in horizons from the microsecond to the second.

Distributed energy resources (DERs) and distribution network reconfiguration have considerable effects on both the economic and operational performance of distribution networks. However, the uncertain nature of renewable energy sources (RESs), wind energy, for instance, can bring about serious challenges to the distribution system operators and distribution companies (DisCos). Therefore, a suitable methodology is a matter of the utmost importance to handle the uncertainty of RESs. In addition, DisCos can benefit from the utilisation of energy storage technologies to increase the penetration of RESs into the system. In this regard, this study proposes a risk-averse energy management strategy (RA-EMS) in the presence of DERs, while the impact of uncertainties of RESs on the optimal configuration of the network is investigated. The uncertainty of RESs is modelled through the information gap decision theory, which has significant advantages such as low computational burden, no need for probability density function, and exact results compared to other methodologies for uncertainty handling. The proposed RA-EMS model is implemented on the IEEE 33-bus distribution system, and its superiority over the scenario-based stochastic programming is demonstrated. The robust configuration of the system against RESs' uncertainty is obtained for different levels of uncertainty radius.

Wind and photovoltaic are now the fastest growing renewable energy sources (RESs) around the world and mitigation of the inherent intermittency of such energy sources with battery energy storage systems (BESS) has gained attention in recent years. In this paper, a novel voltage security stochastic optimal BESS allocation (VSC-SOBA) problem is proposed in integrated transmission and distribution (T&D) systems while including realistic models of distributed energy resources. The scheduling problem is studied under stochastic framework by considering the wind power uncertainty. Besides, hourly profiles of load as well as wind and photovoltaic RESs are taken into account. Maximizing loading margin (LM) with the best allocation of different BESSs types is the main objective of this paper. Moreover, the allocation impact on voltage deviation of load buses is investigated. The mixed integer non-linear programming based model is implemented in GAMS software considering P, Q, and simultaneous P+Q BESS control. The performance of the proposed method is examined on T&D 16-bus, 62-bus, 286-bus, and large-scale 1142-bus test systems and the findings show that in the case of VSC-SOBA, the desired LM is maximized in the presence of optimal BESS control variables.

This paper proposes a new framework for corrective voltage control (CVC) of power systems. It ensures a desired loading margin (LM) after encountering severe contingencies while minimizing the corresponding control costs. The framework is divided into primary CVC (PCVC) and secondary CVC (SCVC) stages for restoration of voltage stability and ensuring a desired LM. These stages are based on the sequence and quickness of the control actions required in post-contingency state of the system. The PCVC sub problem deals with the condition faced by a power system subject to voltage instability as the result of severe contingencies. Such control is merely devised to restore system stability. Next, in the SCVC sub problem that follows PCVC, the system operating point is modified such that a desired LM is ensured, and hence voltage security of the system is achieved. The active and reactive power redispatch of generation units and involuntary load curtailment are employed along with the voluntary demand-side participations as control facilities in PCVC and SCVC sub problems, by deploying a proper voltage dependent static model for loads. The proposed framework is examined on the IEEE 118-bus system. The numerical results substantiate the effectiveness of the proposed approach.

Technology penetration analysis problem has turned to be a controversial issue for power system utilities with increasing the penetration of renewable energies. In the other hand, the more concern regarding the climate change increases, the more important the clean energy resource penetration becomes in power system studies. Therefore, in this paper a corrective integrated transmission and distribution co-simulation approach is proposed for scenario analysis of different technology penetrations such as electric vehicles, wind farms and solar panels in distribution side. The transmission level is modelled in MATPOWER toolbox while the distribution level is modelled in OpenDSS software where these two models are connected through a Pyhton-based interface. The proposed model is applied on IEEE 118-bus test system as transmission level and IEEE 123 nodes and IEEE 8500 nodes as distribution networks. The scenario analysis results demonstrate the nonlinear and contradictory behavior of different technology penetration which has been overlooked in the previous literature.

The load increment as the consequence of electrification of everything in the modern distribution networks, makes it imperative to analyze the interactions between electric power transmission and distribution (T&D) systems. Such a massive electrification may deteriorate voltage profiles and power exchanges, which results consequently in lower grid efficiency. As a low-cost and effective solution, the installed controllable devices for voltage in distribution networks such as under-load tap changer (ULTC) and capacitor banks can be optimized to overcome the aforementioned challenges. This paper proposes a new integrated transmission and distribution co-simulation platform where the aggregated loads in transmission system simulator (i.e. MATLAB) are replaced by a distribution network modeled in distribution system simulator (OpenDSS) through a Python interface. The overall T&D system efficiency is then optimized while maximizing loading margin (LM) and minimizing the total system power losses are contemplated as two objective functions. The proposed approach is applied on a constructed T&D grid with 68K nodes and the results demonstrate that the efficiency of the T&D grid can be improved by optimal setting of the control variables.

In this paper, a stochastic optimal transmission switching (SOTS) model is developed considering the uncertainty of load, wind power and photovoltaic generation, while minimizing the grid vulnerability. Scenario reduction technique is used for alleviating computational burden of the developed SOTS model. A three-step procedure, named the vulnerability-oriented SOTS (VO-SOTS), is proposed to consider the set of critical transmission lines, i.e., lines whose outage imposes the largest load curtailment. Moreover, to control the negative impacts of the uncertainties, conditional value at risk (CVaR) is used as the risk measure. The proposed VO-SOTS model is formulated as a mixed integer non-linear programming optimization problem and solved using GAMS optimization package. Also, the VO-SOTS model is implemented on the IEEE 118-bus, IEEE 300-bus, and large-scale IEEE 2869-bus standard test systems. Simulation results for these cases show that considering the set of critical lines allows the proposed VO-SOTS model to reduce generation costs while effectively minimizing system vulnerability and risk. In addition, it is shown through extensive case studies that switching off a line based on the proposed three-step VO-SOTS does not increase the grid’s exposure to vulnerability, in contrast to the conventional OTS.

In this paper, a comprehensive review of recent researches carried out in the area of optimal reactive power dispatch (ORPD) is presented. The literature review consists of a classification of ORPD problem, along with a survey on the strengths and weaknesses of different models proposed previously. Also, a stochastic multi-objective ORPD (SMO-ORPD) model is studied under load and wind power generation uncertainties. A two-stage stochastic model is employed for dealing with the uncertainties. The considered objective functions are real power losses (PLs) and operation & maintenance (O&M) cost of wind farms (WFs). The proposed SMO-ORPD problem is solved by considering voltage stability index (i.e. Lmax index). Besides, e-constraint technique and fuzzy satisfying approach are employed to pick up the best compromise solution. Two cases are considered as follows: (i) voltage stability relaxed SMO-ORPD, and (ii) voltage stability constrained (VSC) SMO-ORPD. In this study, reactive power compensation devices are modelled as discrete control variables. Hence, the proposed SMO-ORPD problem is modelled as a mixed-integer non-linear programming optimisation problem. The SMO-ORPD problem is implemented in GAMS environment, and it is examined on the IEEE 30 and 118-bus test systems. The numerical results substantiate that in the case of VSC-SMO-ORPD, active PLs are higher than the other case, whereas O&M cost of WFs is not different considerably.

In this paper, a robust scheduling model is proposed for combined heat and power (CHP)-based microgrids using information gap decision theory (IGDT). The microgrid under study consists of conventional power generation as well as boiler units, fuel cells, CHPs, wind turbines, solar PVs, heat storage units, and battery energy storage systems (BESS) as the set of distributed energy resources (DERs). Additionally, a demand response program (DRP) model is considered which has a successful performance in the microgrid hourly scheduling. One of the goals of CHP-based microgrid scheduling is to provide both thermal and electrical energy demands of the consumers. Additionally, the other objective is to benefit from the revenues obtained by selling the surplus electricity to the main grid during the high energy price intervals or purchasing it from the grid when the price of electricity is low at the electric market. Hence, in this paper, a robust scheduling approach is developed with the aim of maximizing the total profit of different energy suppliers in the entire scheduling horizon. The employed IGDT technique aims to handle the impact of uncertainties in the power output of wind and solar PV units on the overall profit.

Due to the development of renewable energy sources (RESs), maximization of hosting capacity (HC) of RESs has gained significant interest in the existing and future power systems. HC maximization should be performed considering various technical constraints like power flow equations, limits on the distribution feeders' voltages and currents, as well as economic constraints such as the cost of energy procurement from the upstream network and power generation by RESs. RESs are volatile and uncertain in nature. Thus, it is necessary to handle their inherent uncertainties in the HC maximization problem. Wind power is now the fastest growing RESs around the world. Hence, in this paper a stochastic multi-objective optimization model is proposed to maximize the distribution network's HC for wind power and minimize the energy procurement costs in a wind integrated power system. The following objective functions are considered: 1) Cost of the purchased energy from upstream network (to be minimized) and 2) Operation and maintenance cost of wind farms. The proposed model is examined on a standard radial 69 bus distribution feeder and a practical 152 bus distribution system. The numerical results substantiate that the proposed model is an effective tool for distribution network operators (DNOs) to consider both technical and economic aspects of distribution network's HC for RESs.

With the growth of emerging new technologies in distribution networks, such as wind power, photovoltaic or DGs, the need for simultaneously investigation of these networks alongside transmission networks is highly important. In this paper, an efficient framework is proposed to understand seamlessly transmission and distribution (T&D) interactions while including detailed models for distributed energy resources. Wind power, photovoltaic and load are investigated according to their 24h profile. Besides, wind power uncertainty is considered via scenario based method. Finally, Battery Energy Storage Systems (BESSs‘) are used to track probabilistic renewable generation and minimize the total system power losses. The modified IEEE 9-bus test system is selected as a transmission network and 3 simple 8-bus radial feeders are selected as the distribution side networks. The problem modeled as a mixed integer nonlinear programming (MINLP) optimization problem and handled via GAMS programming language. The results show that with high penetration of renewable sources at distribution level, rescheduling main generators increases system energy efficiency. Moreover, BESS mitigates the impact of renewable sources volatility on both transmission and distribution levels while reducing power losses during peak hours.

Due to nonlinear and discrete variables and constraints, optimal reactive power dispatch (ORPD) is a complex optimization problem in power systems. In this paper, the purpose is to solve multi objective ORPD (MO-ORPD) problem considering bus voltage limits, the limits of branches power flow, generators voltages, transformers tap changers and the amount of compensation on weak buses. The objectives of this paper are real power losses and voltage deviations from their corresponding nominal values, which are conflicting objectives. Because of the stochastic behavior of loads, the MO-ORPD problem requires a probabilistic approach. Hence, in this paper, a two-point estimate method (TPEM) is proposed to model the load uncertainty in MO-ORPD problem. Moreover, the proposed method is compared with some other methods such as deterministic approaches and Monte Carlo simulations (MCS). The obtained results approve the efficiency of the proposed methodology. The proposed models are implemented and solved using GAMS optimization package and verified using IEEE 14-bus and IEEE 30-bus standard test systems.

Optimal reactive power dispatch (ORPD) problem is an important problem in the operation of power systems. It is a nonlinear and mixed integer programming problem, which determines optimal values for control parameters of reactive power producers to optimize specific objective functions while satisfying several technical constraints. In this paper, stochastic multi-objective ORPD (SMO-ORPD) problem is studied in a wind integrated power system considering the loads and wind power generation uncertainties. The proposed multi objective optimization problem is solved using e-constraint method, and fuzzy satisfying approach is employed to select the best compromise solution. Two different objective functions are considered as follow: 1) minimization of the active power losses and 2) minimization of the voltage stability index (named L-index). In this paper VAR compensation devices are modeled as discrete variables. Moreover, to evaluate the performance of the proposed method for solution of multi-objective problem, the obtained results for deterministic case (DMO-ORPD), are compared with the available methods in literature. The proposed method is examined on the IEEE-57 bus system. The proposed models are implemented in GAMS environment. The numerical results substantiate the capability of the proposed SMO-ORPD problem to deal with uncertainties and to determine the best settings of control variables.

There have been a variety of studies developed to design static Var compensator (SVC) to enhance damping of electromechanical oscillations in power systems. This paper presents a novel method towards an optimal designing of SVC damping controller using shuffled frog-leaping algorithm (SFLA). The optimal parameters of the SVC controller are obtained using SFLA considering different loading conditions. A time-domain objective function is minimized using SFLA to find the optimum controller parameters. The proposed algorithm is implemented on a test system with different loading conditions. The damping characteristics of the presented technique are compared with genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in tuning SVC controller. The comparative analysis demonstrates the superiority of the proposed method on a wide range of operating conditions.

This paper aims to develop a framework to study and optimize the benefits high penetrations of battery energy storage systems (BESS) in the integrated transmission & distribution (T&D) systems. A coupled T&D systems analysis framework is developed through a co-optimization approach. The following objective functions are considered in the proposed optimization approach: 1) Real power losses (PL), 2) Voltage deviation (VD) at load buses, and 3) loading margin (LM) or loadability of system. The framework is utilized to investigate the impacts of high penetrations of distributed energy resources with embedded BESS. Simulations are performed with 100 and 50 percent of BESS charging capacity. The proposed framework is examined on the IEEE 118-bus system. The results show that the installation of distributed BESSs at the distribution side, decrease the PL and VD and also improve the system total LM.

One of the most principle optimization problems which gained the attention of power system operators around the world is optimal power flow (OPF). The OPF basically performs an intelligent power flow and optimizes the system operation condition by optimally determination of control variables. It also considers a specific set of operational constraints and technical limits for this aim, which guaranties both feasibility and optimality of the scheduled operation condition. Generally, this problem can be categorized into two main sub-problems, i.e. optimal reactive power dispatch (ORPD) and optimal real power dispatch, which are differ in their aims and control variables. This chapter deals with the first one, ORPD, which has significant impact on power system security. ORPD is modeled as an optimization problem with nonlinear functions and mixed continuous/discrete variables. Thus, it is a complicated optimization problem. The multi-objective ORPD (MO-ORPD) problem is studied, taking into account different operational constraints such as bus voltage limits, power flow limits of branches, limits of generators voltages, transformers tap ratios and the amount of available reactive power compensation at the weak buses. Three different objective functions are considered in the proposed MO-ORPD framework, which are minimizing total active power losses, minimizing voltage variations and minimizing voltage stability index (L-index). These conflicting objectives are optimized via e-constraint method. In order to model the stochastic behavior of demand and wind power generation, it is necessary to modify the MO-ORPD problem, and develop a probabilistic approach to handle the uncertainties in MO-ORPD problem. Hence, a two-stage stochastic MO-ORPD (SMO-ORPD) is suggested to handle the load and wind power uncertainties in the MO-ORPD problem. In the proposed two-stage stochastic optimization model, the decision variables are classified into two categories, namely, “here and now” and “wait and see” variables. The optimal values of “here and now” variables should be known before realization of scenarios, and therefore, their values are the same for all scenarios while the optimal values of “wait and see” variables are based on the realized scenario, and hence their values are scenario dependent. Moreover, in order to examine performance of the proposed SMO-ORPD and the impact of wind power generation on the results of SMO-ORPD, deterministic ORPD (DMO-ORPD) has also been solved in two modes: DMO-ORPD without wind farms (WFs) and any uncertainty, for the sake of comparison with the available methods in recent literature, and DMO-ORPD with WFs. In this chapter the reactive power compensation devices are modeled as discrete/continuous control variables. DMO-ORPD and SMO-ORPD are formulated as mixed integer non-linear program (MINLP) problems, and solved by General Algebraic Modeling System (GAMS). Also, the IEEE 30-bus standard system is utilized for evaluation of the proposed MO-ORPD models.