Renewable Distributed Generation Units Research Articles

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

Optimal Distributed Generation Planning Considering Economic and Operational Aspects

Distributed generation (DG) has gained prominence in distribution systems in recent decades due to its capability to reduce carbon footprint, improve power quality, voltage support and maintain a sustainable system. Although DG has numerous benefits, unplanned DG integration can adversely impact the power system parameters and the distribution company’s (Discom) income. This work addresses optimal site and sizing planning for renewable and dispatchable DG, considering long-term economic and technical benefits. A multi-objective (MO) problem is formulated to reduce the cost associated with DG units, their operation and maintenance (OM), fuel, emission, and operational objectives. The impacts of hourly variation in the power generation of renewable DG units and load demand profiles are investigated through case studies. Annual load growth and load modeling are included in the study to have a pragmatic model. An improved particle swarm optimization (IPSO) technique having chaotically varying inertia weight is used to determine the optimal solution. Profit and payback period (PP) calculations for the purchase and sale of electricity on the contract price for the project duration are discussed in detail. The proposed approach has been demonstrated to be effective through simulation studies conducted on the modified IEEE-33 bus and the real Italian distribution system.

Clustering-based reliability assessment of smart grids by fuzzy c-means algorithm considering direct cyber–physical interdependencies and system uncertainties

The steadily growing deployment of cyber systems in smart grids (SGs) has highlighted the impacts of cyber–physical interdependencies (CPIs). Although much attention has been paid to the reliability evaluation of SGs considering the system uncertainties and CPIs by Monte Carlo simulation (MCS), the computation time is one of the essential challenges of MCS-based methods. This research tries to overcome the discussed challenge by developing a new clustering-based reliability evaluation method considering the direct CPIs (DCPIs) and stochastic behaviors of renewable distributed generation units (RDGUs) and the demand side. In the proposed method, the Fuzzy c-means (FCM) clustering algorithm has been used to reduce the number of scenarios for uncertain parameters besides the DCPIs. Determining the appropriate alternatives for the number of clusters of stochastic parameters in various cases based on cyber network topologies, DG technologies, and the penetration levels of RDGUs is another contribution of this paper. Test results of applying the proposed method to an actual test system illustrate the advantages of the proposed clustering-based method. The comparison of the proposed method with MCS shows the computation time could be reduced from 21658 s to 210 s (99%), while less than 1% EENS error appears.

Power distribution network design considering the distributed generations and differential and dynamic pricing

In this study, a power distribution network design model is developed considering voltage control, as well as differential and dynamic pricing schemes. The objective is to maximize power distribution network profits under uncertain demands and parameters, such as power generation capacity, investment and generation cost of renewable distributed generation units, and the penalty budget for unsatisfied demands. The model determines the percentage of the power supply to the customer groups, the amount of power supplied to each customer group, the technology and capacity of renewable distributed generation units, and the prices offered. The proposed pricing scheme is differential for different types of customers with allowable voltage limits. The price is dynamic according to the planning time, including low, medium, and peak times. A fuzzy stochastic programming approach is developed to solve this problem. By simultaneously using triangular crisp numbers and probabilistic variables, the fuzzy stochastic programming integrates the fuzzy and stochastic approaches to deal with the uncertainty of demand and parameters. The numerical analysis shows that, as the confidence level decreases, the capacity of renewable distributed generation units and the profit increase. The results also show that when the decision maker responds to the uncertainty in demand with a high confidence level, the optimum profit can be obtained; approximately 55.90% of the power is supplied to low-demand customers.

A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage

Abstract 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.

A fast-specialized point estimate method for the probabilistic optimal power flow in distribution systems with renewable distributed generation

The increasing presence of renewable distributed generation (DG) units such as photovoltaic and wind power generation is a major challenge for the suitable operation of the electrical distribution systems (EDSs). Uncertainties of renewable DG units and loads, related to the stochastic nature of solar irradiation, wind speed, and consumer behavior, require efficient tools that help the distribution system operator to properly define a control plan of the EDS. Within this framework, the probabilistic optimal power flow (POPF) provides statistical information(e.g. voltage profile, power flows, and power losses) according to the variation of the stochastic variables (e.g. power demand and injection of generation units). Many available POPF methods have been designed for transmission systems and/or are based on Monte Carlo simulation (MCS), which requires a high computational effort. On the other hand, other approaches adopt analytical methods, which are not applied considering the characteristics of distribution systems. This paper proposes a fast-specialized point estimate method for the POPF in EDSs with the presence of renewable DG units, based on a linearization of the Branch Flow equations and Hong’s point estimate method. Due to its convex nature, the advantage of the proposed method is to use well-established linear programming commercial solvers to solve the problem. Numerical results using the IEEE 69-bus and a real EDS demonstrate the efficiency in terms of computational burden and accuracy of the proposed method compared to MCS and Cumulant approaches.

Multi-objective energy management of smart homes considering uncertainty in wind power forecasting

Smart homes are one of the effective parts of a smart grid. A residential smart grid operates properly when each of smart homes is managed appropriately. The energy management system of a home has to simultaneously manage the operational time of appliances and how to use the energy of an electric vehicle and home renewable distributed generation units like a wind turbine. So, the energy management of smart homes in a residential smart grid is studied in this paper. The wind turbine is considered as the energy source of the smart home while the electric vehicle can operate as a producer/consumer unit in discharge/charge mode. The uncertainty model of wind turbine and electric vehicles are also considered in the proposed method for energy managing. The real-time pricing tariff is utilized for applying the demand response program. The electricity cost of consumers and the peak to average rate of demand are formulated to achieve the best schedule of devices of smart homes. Multi-objective dragonfly algorithm is utilized for optimizing the technical and economic objective functions. After finding the optimal Pareto front, the analytical hierarchy process method is used to select the best operational schedule of smart homes. The proposed method is evaluated in a sample smart grid. Numerical results show that the proposed management method has considerable efficiency in improving the performance of the smart grid.

Combined Firm and Renewable Distributed Generation and Reactive Power Planning

The benefits of integrating Distributed Generation (DG) into the distribution networks depend on the characteristics of different types of DG units, loads and Reactive Power Sources (RPS). These benefits can be optimized if the firm DG units such as biomass energy and renewable DG units such as photovoltaic (PV) and wind system are optimally sized, located and coordinated with reactive power sources. In this paper, by assuming that the Distribution System Operator (DSO) has got the ownership and operation of DG units and RPS, a new planning strategy is proposed for determining the optimal placement and rating of DG units and RPS. This strategy overcomes the challenge of intermittency of renewable production in order that this planning will assist the system operators in defining the better integration strategies of firm and intermittent energy systems and reactive power sources in distribution networks. The proposed planning is based on single objective optimization so that it optimizes one of the following objective functions every time: the system energy losses, voltage stability margin, self-adequacy of microgrids defined on the distribution system and exchange of active and reactive powers between the distribution system and upstream network. The proposed technique accounts for the uncertainties associated with solar irradiance, wind speed and demand through a probabilistic optimization. The formulation of each planning problem is presented and applied to the 69-bus distribution system. The results of different planning strategies are compared and analyzed. Furthermore, the impact of the planning with each objective function on other indices is evaluated.

Toward sustainable microgrids with blockchain technology-based peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach

Microgrids with renewable distributed generation units are a promising solution for reducing investment cost and greenhouse gas emissions in the face of rapidly rising energy demand and recent organizational concerns regarding social and environmental issues. However, microgrids closely located to demand areas cannot easily balance power supply and demand owing to the intermittency of renewable energy generation and the barriers to connecting with the main electricity grid. To overcome this problem, this study addresses a sustainable microgrid design problem, where blockchain technology is used for peer-to-peer energy trading in the microgrid. The adoption of the blockchain technology in peer-to-peer energy trading ensures the security and sustainability for participants in the microgrid. It enables the participants to take control of the energy system without the need for a central regulatory authority. The problem entails decisions regarding not only the power flow among entities in the grid and the number, location, and capacity of renewable distributed generation units but also the price mechanism for peer-to-peer trading. A sustainable network microgrid is modeled by considering economic and environmental objectives with social constraints related to maximizing demand satisfaction of consumers. A fuzzy multi-objective programming model is proposed to tackle the variability in the capacity of renewable distributed generation units and demand load. The proposed model is solved by a genetic algorithm. Numerical experiments are conducted to evaluate the feasibility of the proposed model in decision-making regarding microgrid design, the impact of peer-to-peer trading on the total profit of the microgrid, and the applicability of the developed solution approach.

Sustainable microgrid design considering blockchain technology for real-time price-based demand response programs

In recent years, rapidly increasing electric power demand and organizational concerns regarding social and environmental issues have resulted in significant attention being given to microgrids. However, sustainable microgrids that simultaneously address economic benefits and environmental and social issues have not been broadly explored by researches. This study addresses the sustainable microgrid design problem by leveraging blockchain technology to provide the real time-based demand response programs. Three sustainable objectives (economy, environment, and society) are formulated by a multi-objective mixed integer-linear programming model. A robust fuzzy multi-objective optimization approach is proposed to determine the optimal number, location, and capacity of renewable distributed generation units as well as the equilibrium supply and dynamic pricing decisions under uncertain demand, capacity, and economic, environmental, and social parameters. The proposed model and solution approach are then applied to a case study in Vietnam. We find that blockchain technology-based sustainable microgrid can result in a 1.68% and 2.61% increase of profitability and consumer satisfaction, respectively, and a 0.97% reduction of environmental impacts.

A novel multi-objective modified symbiotic organisms search algorithm for optimal allocation of distributed generation in radial distribution system

This article presents a novel optimization technique to allocate distributed generation (DG) units optimally in radial distribution system (RDS). Three renewable type DG units (such as wind turbine, solar photovoltaic and biomass system) have been integrated in the RDS. In this regard, an optimization problem is formulated considering multiple technical and economic objectives of the DG planning. A new metaheuristic, namely multi-objective modified symbiotic organisms search (MOMSOS), is proposed to solve this optimal DG allocation problem. A chaos-based cross-over operator is introduced in the parasitism phase of the proposed MOMSOS to enhance diversity in the population. The proposed MOMSOS is equipped with hierarchical non-dominated sorting technique which is superior to the existing fast non-dominated sorting strategy in terms of computational complexity. An adaptive penalty function is utilized for constraint handling. The proposed algorithm is tested on some CEC 2009 benchmark test problems to ensure its global optimization capability. Furthermore, performance of the proposed MOMSOS is validated on 69-node RDS and the obtained results are compared with other well-established multi-objective optimization algorithms.

Assessing Socioeconomic Impacts of Integrating Distributed Energy Resources in Electricity Markets through Input-Output Models

In competitive electricity markets, the growth of electricity generated by renewable sources will reduce the market price of electricity assuming marginal cost pricing. However, small renewable distributed generation (RDG) alone cannot modify the formation of electricity prices. By aggregating small RDG units into a Virtual Power Plants (as a single unit market) they are capable of dealing at the wholesale electricity market analogous to large-scale producer following in changes in wholesale prices. This paper investigates the socioeconomic impacts of different type of RDG technologies on Spanish economic sectors and households. To this end, we applied an input-output price model to detail the activities more sensitive to changes in electricity price due to RDG technologies deployment and the associated modifications in income and total output associated with the households’ consumption variation. Detailed Spanish electricity generation disaggregation of the latest available Spanish Input-Output table, which refers to 2015, was considered. It was found that the integration of RDG units in the electricity market project a better situation for the economy and Spanish households. This paper’s scope and information can be used to benefit decision-making with respect to electricity pricing policies.

Distribution System Restoration With Renewable Resources for Reliability Improvement Under System Uncertainties

Integration of renewable distributed generation (DG) units into distribution networks is gaining widespread popularity. However, uncertainties in generation availability associated with renewable DG units pose a major challenge. These uncertainties should be properly addressed to ensure acceptable system performance and improve customer-side reliability. In this article, the reliability assessment of distribution systems embedded with renewable DG sources has been carried out giving emphasis to system uncertainties and optimal restoration strategies. The uncertainties associated with the power output from renewable resources, time-varying load demand, stochastic prediction errors, and random fault events have been accounted for in the restoration optimization formulation for reliability evaluation. In this article, a parameter-free particle swarm optimization (PSO) technique is applied to address the complexity involved in the formulation. Moreover, a problem-specific encoding scheme is also proposed in conjunction with PSO to ensure optimality.

Innovative Volt/VAr Control Philosophy for Future Distribution Systems Embedded With Voltage-Regulating Devices and Distributed Renewable Energy Resources

Conventional voltage-regulating devices such as load tap changers, voltage regulators, and local capacitor banks are commonly used for voltage-regulation purposes in medium-voltage distribution systems. Although such devices exhibit excellent capability for Volt/VAr control (VVC) support, fine-tuning of their controller parameters and enactment of Volt/VAr support by renewable distributed generation (DG) units is essential for effective and efficient operation of future distribution systems embedded with wind and solar-Photo-voltaic (PV) units. In this paper, an innovative algorithm is conceptualized for control set-point re-adjustment of different VVC devices for coordinated voltage control in distribution systems. The intended VVC strategy is able to minimize the voltage variations including voltage drop and rise cases mainly caused by power output of renewable DG units that cannot be solved by voltage regulating devices due to their time-delayed operation. Moreover, the control algorithm is proposed to be implemented online using advanced distribution management system for effective voltage control. The simulation studies are carried out using the test distribution system derived from an electricity network in New South Wales, Australia. The simulation results show that voltage regulation can effectively be achieved in renewable rich future distribution systems by applying the proposed tuning algorithm, which has been designed based on a new VVC philosophy.

Reliability optimization of smart grid based on optimal allocation of protective devices, distributed energy resources, and electric vehicle/plug-in hybrid electric vehicle charging stations

There is a knowledge gap to introduce a comprehensive simultaneous optimal allocation of protective devices, distributed generation units, and electric vehicle/plug-in hybrid electric vehicle charging stations. This paper tries to fill such a knowledge gap by proposing a method that simultaneously considers all discussed optimization variables. In the proposed hierarchical optimization method, three stages and seven scenarios are introduced. Because of the complexity increment of optimization problem as well as the number of the optimization variables, it is useful to have the criteria for the results cross-checking. In the proposed hierarchical optimization method, the third stage optimization results which consist of all three types of optimization variables are examined with those of first and second stages.To illustrate the advantages of the proposed method, it applies to the IEEE 33-bus and IEEE 69-bus test systems in the widespread presence of non-renewable and renewable distributed generation units, e.g. wind turbine and photovoltaic power plants, and electric vehicles. The hierarchical optimization method comparison results of different stages determine the importance of any variable type on the system reliability. By getting insight into how any variable affects the system reliability, it is possible to investigate the most effective corrective and improving solutions.

Multiobjective robust fuzzy stochastic approach for sustainable smart grid design

Smart grids are an effective solution for the rapidly increasing power demand. This study attempts to solve the sustainable smart grid design problem, where three indispensable sustainability dimensions, economy, environment, and society, are considered simultaneously. A multiobjective robust fuzzy stochastic programming approach is presented to minimize the economic, environmental and social costs of the network under uncertain scenarios and parameters. The objective is to determine the optimal number, location and capacity of renewable distributed generation units as well as the dynamic electricity pricing and energy resources scheduling in a sustainable smart grid. The proposed method is applied to a Vietnamese power company in the residential sector. The results indicate that demand response with dynamic pricing reduces environmental and social costs by 3%. Moreover, the proposed model efficiently tackles both uncertainties in scenarios and parameters with a total cost lower than other models.

Monte-Carlo simulation based multi-objective optimum allocation of renewable distributed generation using OpenCL

Monte-Carlo simulation (MCS) is the most accurate technique for considering the stochastic nature of renewable energy resources in power system analysis and planning. However, due to its heavy computational burden, MCS is rarely utilized when solving the multi-objective renewable distributed generation (DG) allocation problem. In order to address this problem, this paper proposes a novel methodology to exploit the massively parallel architecture of graphics processing units (GPU) in a way that enables the use of MCS when solving the multi-objective renewable DG allocation problem. First, the renewable DG allocation problem is formulated as a multi-objective optimization problem to minimize the lines losses and the costs pertaining to installing renewable DG units in the distribution network. Then, a parallelized implementation of NSGA-II using OpenCL is described in details to solve the formulated multi-objective renewable DG planning problem. The feasibility and effectiveness of the proposed methodology are validated using the IEEE 32-bus test system and two real distribution test systems. The results show that the proposed parallelized implementation can enable the use of MCS for modelling the generation and demand uncertainties when solving the multi-objective renewable DG allocation problem using a metaheuristic approach.

A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation

This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation.

An energy-based analysis of reduced-order models of (networked) synchronous machines

ABSTRACT Stability of power networks is an increasingly important topic because of the high penetration of renewable distributed generation units. This requires the development of advanced techniques for the analysis and controller design of power networks. Although there are widely accepted reduced-order models to describe the power network dynamics, they are commonly presented without details about the reduction procedure. The present article aims to provide a modular model derivation of multi-machine power networks. Starting from first-principle fundamental physics, we present detailed dynamical models of synchronous machines and clearly state the underlying assumptions which lead to some of the standard reduced-order multi-machine models. In addition, the energy functions for these models are derived, which allows to represent the multi-machine systems as port-Hamiltonian systems. Moreover, the systems are proven to be shifted passive, which permits for a power-preserving interconnection with other passive components.