Band 22, Heft 6 - Special Issue: Emerging Sustainable Green Energy Technologies for Smart Grid; Guest Editors: Surender Reddy Salkuti, Papia Ray and Sandeep Vuddanti

With emergence of automated environments, energy demand increased with unexpected ratio, especially total electricity consumed in the residential sector. This unexpected increase in demand in energy brings a challenging task of maintaining the balance between supply and demand. In this work, a robust artificial ecosystem-inspired optimizer based on demand-side management is proposed to provide the optimal scheduling pattern of smart homes. More precisely, the main objectives of the developed framework are: i) Shifting load from on-peak hours to off-peak hours while fulfilling the consumer intends to reduce electricity-bills. ii) Protect users comfort by improving the appliances waiting time. Artificial ecosystem optimizer (AEO) algorithm is a novel optimization technique inspired by the energy flocking between all living organisms in the ecosystem on earth. Demand side management (DSM) program is modeled as an optimization problem with constraints of starting and ending of appliances. The proposed optimization technique based DSM program is evaluated on two different pricing schemes with considering two operational time intervals (OTI). Extensive simulation cases are carried out to validate the effectiveness of the proposed optimizer based energy management scheme. AEO minimizes total electricity-bills while keeping the user comfort by producing optimum appliances scheduling pattern. Simulation results revealed that the proposed AEO achieved a minimization electricity-bill up to 10.95, 10.2% for RTP and 37.05% for CPP for the 12 and 60 min operational time interval (OTI), respectively, in comparison to other results achieved by other optimizers. On the other hand peak to average ratio (PAR) is reduced to 32.9% using RTP and 31.25% using CPP tariff.

The output power curve of the photovoltaic array presents a multi-peak characteristic under partial shading conditions, which causes the traditional maximum power point tracking technology to fail to guarantee the maximum power output of the photovoltaic cell. In response to this problem, this paper proposes to apply the improved Mayfly Optimization Algorithm (MA) to the maximum power tracking control. By introducing the gravity factor and limiting the search space of male mayfly, the optimization accuracy of the algorithm is enhanced, the vibration of the algorithm near the MPP is reduced, and the occurrence of premature phenomenon is avoided. Three test functions are selected to verify the algorithm, and under the conditions of rapid irradiance changes and complex shadow occlusion, an MPPT model based on the Boost circuit is established to verify the effectiveness of the algorithm. The simulation results show that the improved MA algorithm can effectively converge to MPP under complex shading conditions, and the output efficiency of photovoltaic arrays can be maintained above 99.96%. The average tracking time for different shading patterns is about 0.15 s.

Currently, penetration of the renewable energy sources (RES) like solar photovoltaic (PV) panels, wind turbine-based plants is increasing in the conventional power grid to combat pollution, global warming, and to enhance energy sustainability. Fast power electronic converters are necessary to extract power from these sources which do not have any inertia. When more renewable sources are connected to the power grid, it reduces the effective system inertia which results in unacceptable grid frequency changes for any transient. This leads to frequent tripping, cascading fault, and instability of the overall system which can create large-scale blackouts. This work is related to the generation of physical inertia through the biogas plant and emulates inertia from the dc-link capacitor to control the rate of change of frequency (RoCoF) under abrupt load change. The stored energy in a biogas plant and dc-link capacitor in an AC microgrid (MG) can support momentary power requirement which improves the transient performance of grid frequency under unavailability of PV power. A storage system can help to compensate for abrupt frequency change during transient but due to its higher cost and relatively lesser lifetime, these systems can’t be relied upon in the long run. The proposed scheme of cogeneration and frequency control can provide better performance which is simulated in MATLAB 2013 (b). The control system is implemented in hardware using NI-cRiO 9082 in 500 W AC MG which shows 53.57% improvement in RoCoF which complies with the requirement of IEEE/IEC 60255-118-1.

Under the deregulated market scenario, the electric power industry is rapidly getting transformed due to integration of increasing renewable generation. Profit maximization has always been of highest priority for all the stakeholders. The generation planning problem is an important problem in the operation and planning of power systems which is required to be solved to accomplish the maximum profit. The individual GENeration COmpanies (GENCOs) are envisioning their most profitable Unit Commitment (UC) in the light of the available forecasts of availability of solar and wind power, energy prices and reserve prices during a given scheduling period. This paper presents a UC strategy for GENCOs’ profit with a generation portfolio comprising thermal generation, wind generation, and solar PV generation in different combinations. Priority List Method (PLM) and Modified Particle Swarm Optimization (PSO) with Time-Varying Acceleration Coefficients (PSO-TVAC) solution technique is deployed on the MATLAB platform. The aim is to maximize the profit while satisfying the load demand together with maintaining sufficient reserve at each hour. Sensitivity analysis of profit has also been carried out to showcase the impact of different pricing techniques, different parameters chosen and the different bidding prices for the power dispatched as well as for the reserve power maintained. The observations that emerged through the presented solution model may help develop an insight to the GENCOs to gain maximum profits.

This paper combines evolutionary game theory and Agent-based modelling for finding out the optimal location and size of multiple solar PVs in an unbalanced distribution system. The use of Agent-based modelling in solar PV placement is new and hence provides a valuable contribution. NashDE algorithm is implemented and simulated using an agent-based modelling framework MESA. In MA-NashDE, the optimization variables, i.e., the power system buses, are clustered into several zones using both the Loss Sensitivity Index and the Voltage Sensitivity Index. Each zone is under the control of a Nash/zonal player, and the strategy adopted by the zonal player will evolve through Multi-Agent Differential Evolution (MADE) algorithm. This is a novel study focusing on the PV placement problem by Agent-based Evolutionary Game Theory. The proposed methodology was tested using IEEE 34 and IEEE 123 bus radial distribution network using the OpenDSS-Python COM interface.

Renewable energy resources based hybrid microgrid has emerged as a significant component of the power system. Nevertheless, the control and management of power, voltage and frequency under transition between grid-connected mode (GCM) and islanded mode (IM) is a major concern. This paper proposes a self-sustained and flexible novel cooperative control strategy (CCS) for smart hybrid microgrid (SHMG) under GCM and IM mode operations. In islanded operation of SHMG, conventional droop offers ease of control but its execution with seamless transition between operating modes poses poor voltage regulation and frequency deviation. For this purpose, an intelligent cooperative controller is proposed. It consists of a modified P/f-Q/V droop control (MDC) algorithm along with soft computing based VF-controller unit (VFCU). The proposed control scheme is implemented to regulate voltage and frequency. It also enhances the power quality with unbalanced non-linear load and source intermittencies. The absence of a communication link reduces cost and also eliminates the risk of single point breakdown. The SHMG has been simulated in MATLAB and implemented in real-time. The obtained experimental results demonstrate the competency and high robustness of the proposed scheme.

This paper presents the modeling and control of a non-minimum phase dc-dc boost converter based on the three - state switching cells. In any double stage grid-connected system the converter forms an interface between the photovoltaic source and the inverter. As the control and regulation of the converter output is a vital part of penetration of renewable to grid, therefore, this paper had attempted the control of a converter topology that can reduce the current stress across its switches. But the system becomes highly unstable and complex which has been validated by predicting the limit cycle with a describing function. The Controller design is implemented after reducing the complexity of the system using the Model order reduction principle. H-inf controller being robust in nature is applied for stable and regulated output.

The growing popularity of plug-in electric vehicle (PEV) around the world makes complexity in power sector. The distribution system is subjected to overload due to the random penetration of PEVs in charging depending on their level of state-of-charge (SOC). The accurate calculation and prediction of SOC considering their travel distance makes significant impact on the level of SOC. Therefore, the accurate SOC prediction of PEVs is need of the hour in transportation sector. However, the prediction of SOC allows the PEVs owners to decide the charging/discharging mode or priority based charging. Recently, machine learning techniques are gaining popularity in prediction analysis of different parameters. This article proposes machine learning approaches in combination with Bayesian optimization (BO) for prediction analysis of PEVs SOC. The gradient boosting method (GBM) and random forest method (RFM) are used as machine learning approaches in this work. The energy consumption pattern, different battery capacities and total trip distance of PEVs are included in calculation for the estimation of accurate SOC. A satisfactory result of SOC prediction has been observed using both GBM-BO and RFM-BO. The comparative study of results reveals the performance and efficacy of GBM-BO against RFM-BO in the PEVs SOC prediction analysis. Moreover, the hybrid machine learning techniques with BO performs better than individual machine learning techniques in the prediction analysis of PEVs SOC.

The paper presents a probabilistic and deterministic analysis for parameterization of solar cells to study the electrical behavior based on single diode model. Estimation of electrical parameters is important in design, control, and delivery of solar power through a solar cell. Due to non-linearity and non-convexity of the parameterization problem, the single objective function is transformed into set of sub-problems through Pascoletti–Serafini Scalarization using ε -constraint method. Thus, each sub-problem is minimized to obtain a unique set of points on Pareto front. The results are compared with multi-variable Newton Raphson (NR), Particle Swarm Optimization (PSO), and Black Widow Optimization (BWO) based on convergence accuracy, precision and ability to trace non-convex region. Solarex MSX 83 (36 cells) is considered as the test case for the validation of deterministic optimization models under Standard test Conditions (STCs). Electrical characteristics are plotted under STC (1000 W/m 2 , 1.5 A.M. spectrum, 25 °C) which shows fair agreement with the actual experimental curves present in the datasheet. The results obtained from the proposed bi-objective minimization algorithm shows a better convergence response with an additional benefit of tracing the convexity of the problem. Moreover, the proposed technique also ensures a good fit as suggested by the statistical means. Finally, a probabilistic model is proposed for single diode model of a solar cell in the presence of parametric uncertainty. Random samples of uncertain electrical parameters are obtained using Latin hypercube and Monte Carlo sampling methods to estimate the probability of the output response. It can be concluded that the objective function shows a bi-modal distribution under parametric variations which may arise due to measurement error, shading losses, surface defects, and manufacturing issues.

In recent times, producing electricity with lower carbon emissions has resulted in strong clean energy incorporation into the distribution network. The technical development of weather-driven renewable distributed generation units, the global approach to reducing pollution emissions, and the potential for independent power producers to engage in distribution network planning (DNP) based on the participation in the increasing share of renewable purchasing obligation (RPO) are some of the essential reasons for including renewable-based distributed generation (RBDG) as an expansion investment. The Grid-Scale Energy Storage System (GSESS) is proposed as a promising solution in the literature to boost the energy storage accompanied by RBDG and also to increase power generation. In this respect, the technological, economic, and environmental evaluation of the expansion of RBDG concerning the RPO is formulated in the objective function. Therefore, a novel approach to modeling the composite DNP problem in the regulated power system is proposed in this paper. The goal is to increase the allocation of PVDG, WTDG, and GSESS in DNP to improve the quicker retirement of the fossil fuel-based power plant to increase total profits for the distribution network operator (DNO), and improve the voltage deviation, reduce carbon emissions over a defined planning period. The increment in RPO and decrement in the power purchase agreement will help DNO to fulfill round-the-clock supply for all classes of consumers. A recently developed new metaheuristic transient search optimization (TSO) based on electrical storage elements’ stimulation behavior is implemented to find the optimal solution for multi-objective function. The balance between the exploration and exploitation capability makes the TSO suitable for the proposed power flow problem with PVDG, WTDG, and GSESS. For this research, the IEEE-33 and IEEE-69 low and medium bus distribution networks are considered under a defined load growth for planning duration with the distinct load demand models’ aggregation. The findings of the results after comparing with well-known optimization techniques DE and PSO confirm the feasibility of the method suggested.

Demand response (DR), an integral part of the smart grid, has great potential in handling the challenges of the existing power grid. The potential of different DR programs in the energy management of residential consumers (RCs) and the integration of distributed energy resources (DERs) is an important research topic. A novel distributed approach for energy management of RCs considering the competitive interactions among them is presented in this paper. The impact of participation of RC’s in price-based (PB) and incentive-based (IB) DR programs is investigated using game theory. For this, an energy management optimization problem (EMOP) is formulated to minimize electricity cost. The utility company employs electricity price as a linear function of aggregated load in the PB DR program and an incentive rate in the IBDR program. RCs are categorized into active and passive users. Active users are further distinguished based on the ownership of energy storage devices (SD) and dispatchable generation units (DGU). EMOP is modeled using a non-cooperative game, and the distributed proximal decomposition method is used to obtain the Nash equilibrium of the game. The results of the proposed approach are analyzed using different case studies. The performance of the proposed approach is evaluated in terms of aggregated cost and system load profile. It has been observed that participation in PB and IBDR program benefits both the utility and the consumers.