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Future distribution systems can be seen with very high penetration of renewable energy sources (RESs) such as solar photovoltaics and wind turbines on account of diverse techno-economic and social ...

The increasing interest in renewables has encouraged power system planners to include the concept of hybrid energy systems in modern power industry. Besides, the modern power consumers are becoming more concerned about their energy bills which has led to the concept of hybrid energy management systems (HEMSs) for buildings to monitor, control and optimally manage energy consumptions without any waste. In this study, an optimal planning framework is proposed to determine optimal capacities and sharing of hybrid energy conversion systems (HECS) such as wind turbine, solar photovoltaic, battery energy storage and the utility grid. The objective is to maximize the net present value of the project/system which includes the cost of annual investment, operation and maintenance costs of HEMS expected to have incurred in the planning period. All the costs and parameters are considered in the Indian context, and Genetic Algorithm (GA) is adopted to solve this proposed planning framework. The simulation results obtained are compared with same obtained for conventional houses in India. The comparison shows that the proposed framework effectively reduces the electricity bills while improving its reliability.

The integration of optimally placed distributed generation (DG) units in a distribution system can contribute significantly in loss reduction among distribution feeders. In practice, this loss reduction may provide associated benefit to load points (consumers) though they are not contributing for the same. The loss allocation strategy should be such as to benefit DG owners who have actually contributed toward loss reduction. This paper presents a new circuit theory-based loss allocation method for active distribution systems. The method first employs branch-oriented approach to decompose cross-terms of power loss among contributing load points. Thereafter, the contributing currents of DGs are determined using superposition principle to allocate their remuneration/penalty. The proposed method is applied to 33-bus test distribution system. The application results highlight the importance of the proposed method.

The rapid growth of renewables in modern distribution networks results in the spilling of energy due to the limited hosting capacity of these networks, violation of system constraints, reduced network efficiency, and improper utilization of resources. Battery energy storage system (BESS), in spite of its high cost, a shorter life and complex control, offers a flexible solution for the problem. In this paper, a multiobjective nested optimization framework is developed for the simultaneous optimal allocation of multiple solar photovoltaics (SPVs) and BESSs in the distribution networks. The framework involves a two-layered structure; the outer layer provides tentative planning solutions to the inner layer that optimizes the desired objectives of network operations and then returns the functional values back to the outer layer. The purpose of the inner-layer is to satisfy the operational constraints of the networks and ensure the optimal utilization of BESS capacities, suggested by the outer layer, at the time of planning itself. A new BESS operating strategy is proposed for optimum utilization of BESS. The nested multiobjective optimization problem is handled by suggesting a new weighted sum approach in conjunction with a recently developed swarm intelligence-based algorithm, i.e. moth search optimization. Overall, the proposed deterministic model essentially ensures the high penetration of SPVs and the optimal utilization of BESSs to justify their installation. The optimization model is investigated on a benchmark 33-bus test distribution network. The application results highlight enhanced energy efficiency, peak load shaving, high renewable penetration, voltage profile improvement, and mitigation of reverse power flow while effectively absorbing the excess renewable power generation during light load hours.

The major concerns of plug-in electric vehicle (PEV) drivers, endless operation of PEVs from the existing grid and environmental issues essentially require efficient PEVs charging infrastructure by suitably planning fast charging stations (FCSs). The planning of FCS involves several techno-economic objectives considering the operation of FCS owner. However, PEV drivers are interested in service time, whereas the prime concerns for the FCS owners are charging outlets, installed capacity and utilization factor of the FCS. All these attributes play key role in planning and operation of FCS for the given PEV traffic status. This paper proposes a simplified methodology to determine PEV traffic-based planning attributes of FCS. The methodology presents small set of feasible solutions for FCS planner before dealing with the planning problem. The method is direct, non-iterative and does not employ any optimization. The results obtained for a variety of PEVs traffic status highlights the importance of proposed method.

The Multi-objective Non-convex Combined Heat and Power (CHP) scheduling problem of the systems is a complex and arduous problem. This is due to the nonconvex and nonlinear nature of the objective functions involved. This problem aims to simultaneously minimize two vital but conflicting objectives i.e. cost and emission. As the number of operational units and the constraints increase, regular mathematical algorithms are not able to provide a satisfactory solution to the problem. The non-convexity and nonlinearity of these objectives require a good optimization technique to handle it. In this chapter, a comparative analysis of Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) is presented which is used to provide a compromise solution to this Multi-objectives CHP Economic and Emission Dispatch (MOCHPEED) problem. Both objectives of the CHPEED problem are solved using the fuzzy framework. Later, this framework is used to obtain the compromised solution between the both the objectives. The PSO and WOA techniques are tested on three different test systems. In these test systems, valve-point loading and transmission losses are also considered. To highlight the accomplishments of both the techniques, a comparative analysis is done with the results obtained using the latest literature. Statistical data related to costs and emission, and fitness function are included in this chapter which is obtained after simulating the test systems for 100 different unbiased trials. A detailed investigation on various test generating systems shows supremacy of WOA over PSO and other recently established techniques.

Contemporary distributions are now going to underground their overhead distribution lines due to techno-social reasons. Reliability and loss reduction are the two prime objectives for distribution system operation. Since failure rates of ungrounded cables are the function of Joules heating besides their physical lengths, the reliability evaluation of undergrounded distribution systems needs to be reviewed. This paper suggested a suitable modification in existing reliability indices in order to make them more appropriate for underground distribution systems. A multi-objective network reconfiguration problem is formulated to enhance the reliability and performance of distribution systems while duly addressing the variability and uncertainty in load demand and power generation from renewables. The application results on a standard test bench shift the paradigm of the well-known conflicting nature of reliability and network performance indices defined for overhead distribution systems.

Enormous work has been reported in literature to enhance the performance of metaheuristics by modifying their internal mechanisms via intervening their control equations. Usually, these population based techniques are initiated through random creation of individuals (tentative solutions) to preserve adequate diversity in population and then attempts have been made to maintain a better balance between exploration and exploitation of the problem search space. However, it would be much better if some strategy is employed that could divert tentative solutions toward the promising region. This can be possible if the algorithm has some mechanism to develop certain knowledge (super sense) about the quality of decision variables of the problem. This paper presents super sense genetic algorithm (SSGA) that gradually develops super sense during successive genetic evolutions. The accumulated genetic information so obtained is stored and used to divert individuals near the promising region while preserving adequate diversity. SSGA differs to standard genetic algorithm (GA) only on this aspect. SSGA is applied to solve complex combinatorial network reconfiguration problem of radial distribution systems. The application results highlight the effectiveness of proposed GA.

In this article, the problem of simultaneous integration of renewable generation and energy storage systems is framed as a two-level optimization framework in which the siting-sizing and coordinated management of SPVs and BESSs play the role of the leader and the follower respectively in distribution networks (DNs). The first-level of framework is designed to determine optimal sites and sizes of SPVs and BESSs simultaneously to minimize the annual energy loss and load deviation while optimally utilizing the deployed BESSs. The dynamic BESS management (DBM) model is developed in the second-level of framework aiming to minimize multiple objectives such as power loss, node voltage deviation and reverse power flow into the main grid. In the proposed DBM, some new strategies are suggested for optimal dispatch of BESSs. The application results show that the proposed DBM mitigates the intermittency and other related issues of high SPV penetration positively in DNs.

Electric utilities may own capital intensive battery energy storage systems and micro turbine (MT) to cope against the uncertain environment created by large-scale integration of renewable energy sources (RESs) such as solar photovoltaic (SPV) and wind turbine (WT), etc. The introduction of dynamic electricity price model in distribution system has shifted paradigm to optimally manage distributed storage in order to get maximum possible arbitrage benefits along with improvement in system performance. This paper proposes a heuristic method to enhance several techno-economic benefits of distribution systems by coordinating available energy storages in the presence of RESs. Proposed approach independently manages economic operation of BESS whereas MT unit improves load profile of the system. The methodology envisages optimal solution after trying just few combinations of charging and discharging states of BESS by utilizing knowledge base driven approach of optimizing arbitrage benefits under dynamic price signal. Moreover, the untapped potential of MT unit is amicably utilized to reduce system stress. Proposed method considers dynamically varying system states yet the approach is straightforward and non-iterative. This makes the proposed method class apart from existing methods. The application results on a standard 33-bus distribution system highlights importance of the proposed methodology.

Price-based Demand Response (PBDR) programs have been envisaged to motivate customers for changing their consumption patterns by reducing peak demand against financial gains. DR is an emerging strategy to deal with peak demand, energy management, line congestion, reliability, security and economy of contemporary distribution systems. Therefore, it becomes essential to assess overall impact of DR prior to its implementation. However, the job is quite tedious as demand of the customers also depends upon socio-economic factors, human psychology, ambient conditions, etc., besides the dynamic price signal. This paper proposes a comprehensive, more realistic, simple and adaptive mean price-based DR modelling to assess overall impact of PBDR while addressing stochastic demand, and price elasticity of demand (PED) for individual customer. The model suggests dynamic PED to capture the stochastic DR of customers against real-time price signal using historical data. In addition, several DR indices are suggested to assess the impact of DR on several stakeholders. The study on a standard test bench reveals that proposed methodology is simple, robust, quick, and can deliver tangible information in the interest of concerned stakeholders.

Price elasticity model (PEM) is an appealing and modest model for assessing the potential of flexible demand in demand response (DR). It measures the customer’s demand sensitivity through elasticity in relation to price variation. However, application of PEM is partially apprehensible on attributing the adaptability and adjustability along with intertemporal constraints in DR. Thus, this article presents an adaptive economic DR framework with its attributes via a dynamic elasticity approach to model customer’s demand sensitivity. This dynamic elasticity is modeled through the deterministic and stochastic approaches. Both approaches envision the notion of load recovery for shiftable/flexible loads to make the proposed framework adaptive and adjustable relative to price variation. In stochastic approach, a geometric Brownian motion is employed to emulate load recovery in addition to intertemporal constraint of load flexibility. The proposed mathematical model shows what should be the customers elasticity value to achieve the factual DR. The numerical study is carried out on standard IEEE 33 distribution system bus load data to assess its technical and socio-economic impact on customers and is also compared with the existing model.

Many artificial intelligence based optimization techniques have been introduced since the early 60s. This paper provides a brief review of some of the well-known optimization techniques, e.g., Genetic Algorithm, Particle Swarm Algorithm, and Ant Colony Optimization and recently developed techniques, e.g., BAT Algorithm and Elephant Herding Optimization. All these techniques are population-based search algorithms, in which the initial population is created randomly initializing input parameters within the specified range. They approach toward the best solution inspired by the behavior of natural entities. All of these techniques have a potential to provide optimal or near-optimal solutions.

This paper presents a literature review of scheduling techniques and communication protocols (STCP), which make adaptable smart home (SH) capable of implementing demand response. In addition, it presents advantages and disadvantages of various STCP mentioned in the literature. Also, the paper provides the pathway to future researchers for designing of smart homes, as it briefly mentions various specifications of STCP so that one could select according to their application.

The power system is growing from conventional grids to the smarter grids. The development of smart grids has facilitated the communication infrastructure between the grid and the end-user, which enables the grid to communicate with the customer and vice versa. This development motivated the researchers to gain interest in the area of Demand Response (DR). DR is capable to alleviate the economy of the distribution systems, however, its impact percolates to centralized generating stations via transmission systems. An extensive research work has been carried on DR pertaining to vital concerns such as economy, efficiency and reliability of the distribution systems. A brief review of this research is duly addressed by giving special attention to several methodologies, formulations and optimization techniques suggested and also throw some light on the issues and concerns of DR which are yet not being explored, but may provide new dimensions to operate future distribution systems.

In this work operational management of the grid connected AC microgrid with renewables and responsive loads is presented. The operational problem is formulated by combining two different functions; total consumption cost and total operating cost of the prosumer driven microgrid. A new operational model with Demand Side Management (DSM) is proposed to manage the responsive loads in microgrid. A multib-objective problem is formulated for the proposed model and different operational cases with and without DSM are considered. The problem is solved by implementing combined Particle Swarm Optimization and Technique for Order of Preferences by Similarity to Ideal Solution(PSO-TOPSIS). Results are compared for the considered operational cases and found to be encouraging.

The continuous depletion of fossil fuel reserves, strict environmental concerns and economic feasibility give rise to more and more integration of green energy sources in modern power systems. The renewable energy resources based on solar and wind energy are now becoming more popular, however, have inherent variability and intermittency in power generation. This unpredictability reflects on the economic dispatch of conventional generating units. The accurate assessment of uncertain power generation is essential in order to obtain most economic load dispatch from thermal units. In this, a stochastic optimization is emerged as a great tool for measuring uncertainty to realize economic impact on the system. In this article, a scenarios-based stochastic framework is proposed to elucidate dynamic economic load dispatch (SDELD) with amalgamation of wind power generation. The uncertainty associated with wind is modeled in probabilistic manner using Weibull and, for scenarios generation-reduction, a backward reduction technique based on Euclidean distance is utilized. The proposed framework is investigated on standard 15 units generating test system with integration wind power.

The transformation of conventional grids into smart grids evolves bidirectional communication between the customer and utility. This development intends utility to monitor and modify the energy consumption patterns of the customers by floating various Demand Response Programs (DRPs). DRPs are known to be the most effective solution to the various techno-economic problems of the distribution systems. Moreover, its impact percolates through the entire power system. This paper presents an in-depth overview about definition, importance, implementation and outcomes of DR in modern distribution systems. The paper critically analyses and presents several vital issues, concerns and implications of various DRPs being implemented in practice. The paper also highlights some realistic directives to overcome certain limitations of existing DRPs on account of technical, social, economic and geographical concerns.

In this paper, new metaheuristic technique cuckoo search optimization (CSO) is explored for an optimal and robust power system stabilizer (PSS) designing for multimachine power systems and performance is evaluated by comparing with well-established techniques, i.e., genetic algorithm (GA), particle swarm optimization (PSO), and new harmony search optimization (HSO). An eigenvalue-based multiobjective function is implemented for simultaneous control of damping factor and damping ratio to damp out low-frequency local and interarea modes of oscillations. The PSS parameters are so intended that unstable and/or lightly damped mode eigenvalues are shifted to a specified D-shape zone in the left half of the s -plane. The selected metaheuristic techniques GA, PSO, HSO, and CSO are used to obtain PSS parameters for 10-machines, 39-bus New England Power System and the controllers designed are named as GAPSS, PSOPSS, HSOPSS, and CSOPSS, respectively. The performance of all designed PSSs controllers is evaluated by eigenvalues analysis, nonlinear simulations, and performance indices for various operating conditions under different scenarios of severe disturbances and their comparative analysis is observed. The robustness of all designed PSSs controllers is observed by testing them on unseen operating conditions and results are compared for the same. The performance of CSOPSS is found to be better than other controllers designed.

The presence of distributed generation (DG) units in reconfigured distribution systems introduces complexity in loss allocation (LA). DG integration alters feeder power losses so DG owners (DGOs) may be incentivized or penalized accordingly, whereas network reconfiguration alters feeding path of network user’s thus further increases complexity in LA. The LA method should judiciously allocate losses among load points and simultaneously reward DGOs and distribution network operator (DNO) for their contribution towards loss reduction. Moreover, the LA method should take care of the reactive power transactions of network users. This paper proposes a branch current decomposition method (BCDM) based upon circuit theory for allocating losses in active reconfigured distribution system while giving due consideration to reactive power transactions of network users. BCDM considers virtual branch voltage drops which is well supported by analytical treatment. In addition, Superposition is utilized to incentivize/penalize DGOs. A new LA strategy is proposed for fair allocation of loss/loss incentives among network users and DNO. Proposed method is thoroughly investigated under varying loading, load power factors and under varying network topologies. The comparison results obtained on standard test distribution system highlights the importance of proposed method.

In electrical power system, economic load dispatch is a generic operation for optimal sharing of generation units to meet the system load. With the rapid development of the renewable infrastructure and wide encouragement for green energy have emerged hybrid generating systems in power systems. However, there continuous ever-increasing production is creating challenges as well as implicating economic factor also in operation. A collective cost function is considered with the conventional thermal generators along with the consideration of renewable energy sources to envisage the economic factor. For these renewable sources, like wind and solar, the proportional cost, their uncertainty and variability by overestimation and underestimation cost are considered. To achieve this economic day-ahead scheduling, dynamic operation in time scale of 1 h interval is performed. The stochastic nature of wind and solar is modelled by Weibull and Beta distributions, respectively. Moreover, economic optimisation is obtained by a newly developed algorithm called improved fireworks algorithm with non-uniform operator (IFWA-NMO). This introduces adaptive dimension strategy, limiting mapping operator and non-uniform operator. The effectiveness of proposed IFWA-NMO is investigated on standard dynamic economic load dispatch (DELD) system and also employed to solve conventional DELD with wind-solar system.

High penetration of distributed generation (DG) units in contemporary distribution systems can contribute significantly towards loss reduction in distribution feeders. The loss allocation method should reward the benefit of loss reduction to DG owners (DGOs), instead of diverting the same to the load points which actually never contribute towards loss reduction. This paper presents an exact cross-term decomposition method that decomposes each crossed term of power loss/remuneration among contributing network users while independently addressing their active and reactive power transactions. Separate allocation factors are proposed to decompose each individual crossed term involved. The allocation factors are not based upon heuristic, as in several existing methods, but are derived analytically. The method employs superposition principle to evaluate contributing currents of DGs. Thereafter, the losses/remunerations are allocated fairly among network users by suggesting a suitable remuneration strategy for DGOs. The proposed method is investigated on standard test distribution system. The application results are promising compared with other established methods.

This work introduces a new methodology to solve a multiobjective distributed energy resource (DER) accommodation problem of distribution systems by combining a technique for order of preference by similarity to ideal solution and an improved elephant herding optimization technique. A complex real-life multiobjective DER planning problem is formulated and solved using the proposed method. The aim is to determine the optimal sites and sizes of DERs to maximize the overall benefits of utility and consumers. The proposed technique is productively implemented on three small to large-scale benchmark test distribution systems of 33-bus, 118-bus, and 880-bus. The optimal solutions obtained are compared with the methods available in the literature. The comparison shows that the proposed optimization method is promising.