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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 complexity in the design of sustainable microgrids based on renewable energy sources have increased recently due to the involvement of multiple performance indices, scenarios, and stakeholders. At disintegrated level such as rural villages in developing nations, the issue has become more severe concerning the involvement of social and cultural characteristics making the problem multidimensional with multiple objectives. Thus, a proper method which can simultaneously consider the various criteria and differing views of the multiple actors involved in varying scenarios is required. In this paper, a bi-level framework based on the integration of decision making and optimization tool for the design of a rural microgrid with a perspective of sustainable development considering the annual load growth is presented. The first design level utilizes the decision analysis models to determine the best possible energy alternatives for the microgrid design considering the number of key performance indices formulating different scenarios and acts as input to the second level of the framework. A multi-objective computational optimization tool is utilized in the second level to carry out the feasibility analysis of best energy alternative in order to obtain an optimal size with a lower cost accounting yearly load growth. The proposed framework is then implemented to design a sustainable microgrid for an unelectrified village in the hilly region. Most importantly the microgrid design based on the proposed methodology for the target site utilizes the available local resources such as solar, hydro with the dam, hydrokinetics based on river water streams, etc. as the primary energy generating sources with a diesel generator as back up along with battery and pump-hydro storage as storage systems. A total of 23 alternatives depending upon the different combination of energy sources and storage is constituted in a centralized off-grid system. Moreover, four primary performance criteria (social, technical, economic and environment) with 20 sub-criteria in 29 different scenarios are considered for evaluating the microgrid which is highly unlikely to be observed in literature so far. A discursive result illustrating the various profiles (generation and storage system) of the microgrid based on daily, monthly and yearly basis accounting the load growth is also presented.

The integration of demand response programs (DRP) into the microgrid energy management system can enhance the load characteristics by enabling the active participation of consumers to achieve techno-economic benefits. From the microgrid operator's perspective, these DRP can optimize the revenue and accelerate return on investment. The previous research studies do not accurately represent modelling load responsiveness and price elasticities while implementing demand response programs. The accurate assessment of consumer behaviour to market price changes is paramount while modelling the price elasticities. With this viewpoint, a new microgrid energy management problem is proposed with the integration of flexible price elasticity based incentive-driven DRP in this research work. Further, the network flow constraints are considered to evaluate power losses and voltage deviation at each node of the modified IEEE-33 and IEEE-38 bus distribution networks. The comprehensive load model with the combination of linear and nonlinear conservative load responsive models is adopted and derived for each incentive-driven DRP by considering the nonlinear incentive and penalty model. Nine possible demand response scenarios were created, and techno-economic performance indices for each scenario were evaluated. Finally, the Fuzzy Analytic Hierarchy Program is adopted to choose the best alternative based on each techno-economic criterion. The recently reported swarm optimizer Sparrow Search Algorithm is devised for the first time in the microgrid literature to solve the above-proposed microgrid energy management problem. The obtained results are compared with existing metaheuristics to test the algorithm performance.

This paper presents an improvement to the iterative method of electric system cascade analysis (ESCA) for the optimisation of an isolated PV system with battery energy storage system (BESS) for a residential load. The ESCA algorithm is implemented on MATLAB software environment with Final Excess Energy (FEE), Loss of Power Supply Probability (LPSP) and system cost as optimization constraints. The load, temperature and solar radiation profiles are considered for a year, based on historical data. Practical losses in solar radiation reaching the PV collector surface are considered for analysis. Change in PV efficiency due to variation in temperature and change in charge/discharge efficiency of the battery based on current state of charge (SOC) of BESS are taken into consideration which further bolsters the credibility of the ESCA methodology. The ESCA methodology is used to optimize a PV-BESS system for a residential load with average daily consumption of 26.1 kWh. The optimized result obtained from improved ESCA are compared and verified with benchmark HOMER software.

© 2017 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Measurement. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Measurement, vol. 116, pp. 96-105, 2018. doi : 10.1016/j.measurement.2017.11.005.

With the rapid proliferation of non-dispatchable energy sources, the need for demand-side management (DSM) strategies has become crucial to ensure affordability and reliability for end-users. The application of various diversified DSM strategies in the microgrid energy management system (EMS) is gaining popularity. This paper aims to solve the energy management problem of the microgrid in conjunction with both customer-oriented and utility-oriented DSM strategies for the first time in the literature. In light of this, a stochastic EMS framework is developed to implement and analyze the flexible load shaping DSM strategy, price-based, and incentive-based demand response programs (DRPs) in the presence of non-dispatchable energy resources. Further, the flexible price-oriented load model is adopted for price-driven and incentive-driven DRPs to depict the realistic assessment of consumers’ sensitivity to market prices. The scenario construction approach is employed to address the stochastic nature of renewable power generation, market prices, and load demand. With the complexities as mentioned above, the problem needs to be solved with a powerful optimizer sufficiently to enhance energy efficiency and optimize energy utilization. Hence, the recently reported novel metaheuristic algorithm (Black Widow Optimization) is applied to solve the proposed MG energy management problem in the MATLAB environment. The IEEE-34 node distribution feeder-based MG network is modified to study the proposed algorithm's performance, and a detailed analysis of various techno-economic indices is presented. The obtained simulation results are compared with existing popular algorithms to prove the efficacy of the proposed algorithm in terms of convergence, computational time, an optimum solution and the real-time market bid prices were considered in analysis for day-ahead scheduling of microgrid network.

In the current era of sustainable development, energy planning has become complex due to the involvement of multiple benchmarks like technical, social, economic and environmental. This in turn puts major constraints for decision makers to optimize energy alternatives independently and discretely especially in case of rural communities. In addition, topographical limitations concerning renewable energy systems which are mostly distributed in nature, the energy planning becomes more complicated. In such cases, decision analysis plays a vital role for designing such systems by considering various criteria and objectives even at disintegrated levels of electrification. Multiple criteria decision making (MCDM) is a branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives and criteria. This tool is becoming popular in the field of energy planning due to the flexibility it provides to the decision makers to take decisions while considering all the criteria and objectives simultaneously. This article develops an insight into various MCDM techniques, progress made by considering renewable energy applications over MCDM methods and future prospects in this area. An extensive review in the sphere of sustainable energy has been performed by utilizing MCDM technique.

The incorporation of renewables and communication technologies to the utility paves a way for self-sustained microgrids (MG). The volatile nature of these resources, uncertainties associated with the time-varying load, and market prices impose the significance of an efficient energy management system (EMS). So far, the MG optimal operation has been referred to optimize the operating costs only. However, the prospects of incorporating demand-side management (DSM) with the EMS problem and its effect on total operating cost and peak reduction is needed to be evaluated. To fill this gap, the impact of utility induced flexible load shaping strategy on non-dispatchable energy sources is investigated in this paper. A three-stage stochastic EMS framework is proposed for solving optimal day-ahead scheduling and minimizing the operational cost of grid-connected MG. In the first stage, four possible scenarios for solar and wind power generation profiles are created to address the uncertainty problem by considering real-time meteorological data. The second stage deals with the MG system configuration, operational constraints, and assigning DSM load participation data to be incorporated with the objective function. In this regard, the Quantum Particle Swarm Optimization is devised at stage three to obtain the optimal power dispatch configuration for DG units, maximizing the power export to the utility and compare the results with and without incorporating DSM participation for all scenarios. The obtained simulation results show the competence of the proposed stochastic framework about cost reduction by 43.81% with the implementation of the load participation level of 20% DSM.

EHV and HV transmission lines are used widely to transfer power from power generating area to large load centers. Nowadays compensating devices which uses modern power electronic devices are mostly used to improve the power transfer capability of transmission lines. Use of FACTS devices in transmission lines affect protection system very drastically. In this paper, the issues related with distance protection of transmission lines with STATCOM connected at mid-point are addressed. To mitigate the problems associated with distance protection, a novel algorithm based on synchronized measurement for adaptive relay setting is proposed. The power system is modeled in EMTDC/PSCAD software with frequency dependent model of transmission line. The results obtained for the new adaptive setting scheme are accurate, robust and superior in comparison with those obtained from existing distance protection schemes for transmission line with STATCOM.

The increased penetration of renewable energy sources has prompted the integration of battery energy storage systems in active distribution networks. The energy storage systems not only participate in the backup power supply but also have the potential to provide various distributed ancillary services. In this paper, a new bi-level optimization framework is developed to optimally allocate the intense wind power generation units and battery energy storage systems with the provision of central and distributed ancillary services in distribution systems. Two battery energy storage systems and one shunt capacitor are strategically allocated for coordination of wind power generation. One of the battery is deployed at grid substation to participate in central ancillary services whereas second is participating in distributed ancillary services. At level-1, all the distributed energy resources are optimally allocated while minimizing the annual energy loss of distribution systems. Whereas, level-2 performs hourly optimal energy and ancillary services management of distributed resources deployed at level-1. The objectives considered at level-2 are the minimization of hourly load deviation, reverse power flow towards the grid, power loss, and node voltage deviation. The proposed framework is implemented on a real-life Indian 108-bus distribution system for different cases and solved by using a genetic algorithm. The comparison of simulation results reveal the promising advantages of the proposed optimization framework. It provides more energy loss and demands deviation reduction, improved system voltage and power factor at higher wind penetration as compared to the cases in which distributed ancillary services are ignored in the planning stage.

In this paper, the analytical and simulation results of the application of adaptive distance protection scheme for the transmission line incorporating Static Var Compensator (SVC) connected at the mid-point is presented. The mal-operation of the distance protection for the transmission line with SVC at various locations are studied. The simulation results show the under-reaching and over-reaching is more severe with SVC at mid-point of the transmission line. To mitigate the mal-operation of the distance protection, the adaptive scheme is presented based on recursive simulation study. The simulation result with adaptive scheme is outperformed as compared with the conventional scheme. Electro-magnetic Transient Program (EMTP) simulations on two machine system is used to substantiate the claim.

Highly efficient green light emission was observed from Mg doped ZnO nanoparticles synthesized via facile wet chemical route with an average particle size ∼15 nm . The XRD analysis confirmed the growth of wurtzite phase of ZnO nanoparticles. Moreover, the optical properties of these nanoparticles were investigated by different spectroscopic techniques. The resulted nanoparticles exhibit intense green emission peaking at 530 nm (2.34 eV) upon 325 nm (3.81 eV) excitation. The photoluminescence (PL) intensity of visible emission depends upon the doping concentration of Mg. The PL intensity was found maximum up to 4% doping of Mg, and beyond it exhibits a decrees in emission. Furthermore, by varying the band gap from 3.50 to 3.61 eV, the PL spectra showed a near band edge (NBE) emission at wavelength around 370 nm (3.35 eV) and a broad deep level emission in the visible region. The obtained highly luminescent green emission of ZnO nanoparticle would be an ultimate choice for next generation portable optoelectronics device materials.