Wind-based Distributed Generation Research Articles

Wind and photovoltaic systems in sustainable energy mixes: Cost-effective integration approaches

This study introduces an innovative methodology for optimizing the renewable energy sources (RES) mix, specifically wind-based distributed generation (WDG) and photovoltaic distributed generation (PVDG), in smart grids to enhance sustainability and efficiency. Our primary aim is to minimize total system costs, encompassing capital, operational, and maintenance expenses for RES units, alongside grid energy purchases. This methodology incorporates probabilistic models for each generation technology, energy prices, and demand. These models are integrated into a comprehensive multi-state generation-load-price model, addressing uncertainties and enhancing smart grid operations through real-time control. By leveraging the communication infrastructure of smart grids, the approach optimizes investments and boosts RES penetration during the planning phase. Simulation results from a representative distribution system demonstrate the efficacy of this approach in augmenting RES integration within the energy mix and maximizing investment returns. Compared to traditional RES planning, our methodology achieves a significant 2.47-fold increase in RES intake with minimal energy curtailment (8.38 %) and a notable 28.3 % reduction in total costs. These outcomes highlight the potential of our planning approach to significantly enhance the sustainability and economic viability of smart grid systems, proving effective in managing all conceivable system conditions and optimizing the energy mix. This study underscores the benefits of incorporating advanced probabilistic models and real-time control in smart grid planning to meet sustainability goals effectively.

Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

A Nexus among Reliability Improvement of Distribution System with Optimal Placement and Capacity of Wind-Based Distributed Generation Management

Along with the world population growth, the need for a source of electrical energy is higher, so a reliable system with higher capacities is expected. Renewable energy becomes an alternative that supports the goal of reducing the risk of disruption, thus increasing the distribution system’s reliability. A lot of industries and public settlement uses renewable sources of energy as an alternative power supply to comply their energy needs. This research uses wind turbine as a source of renewable energy in the distributed generation (DG). However, the required investment in wind-based DG is commonly considered too costly to deploy and require a proper planning on its placement method. The flower pollination algorithm (FPA) method could be a solution to achieve optimal placement of wind-based DG, thus increase the distribution system’s reliability, which is indicated by minimum energy not supplied (ENS) index.

A fuzzy coordinated energy management strategy for energy storage units in DC multiport energy router

As the proportion of new energy in power systems continues to increase, the widespread use of solar and wind-based distributed generation (DG) power in DC distribution networks will face problems such as new energy consumption, grid stability, and economical benefits in the future. At present, energy management strategies for photovoltaic (PV) grid-connected power generation systems are mainly researched in the field of AC–DC distribution network. This paper provides an advanced energy management strategy of the DC multiport energy router. Considering the battery state of charge (SOC), real-time electricity price (EP), and user-side electricity charge (EC), an energy management scheme based on the fuzzy coordinated control strategy is designed to improve the utilization of energy storage batteries and the economic benefits. A 20 kVA multi-port DC energy router simulation platform is established and verified in MATLAB/Simulink. The results show that the energy management strategies based on fuzzy coordinated controller can solve the multi-objective problem. Compared with traditional control strategies, the fuzzy collaborative energy management strategy has a more comprehensive optimization effect.

Multi-objective optimal planning of wind distributed generation considering uncertainty and different penetration level of plug-in electric vehicles

Despite the many challenges related to the simultaneous surge in wind-based distributed generation (WDGs) and plug-in electric vehicles (PEVs) in modern power systems, they will be a great help in reducing greenhouse gas emissions. Nonetheless, the associated problems such as uncertainty in production-load sides, voltage instability, and power loss augmentation should be reanalyzed. These predicaments can be dealt to some extent with proper siting and sizing of WDGs together with capacitor banks (CBs) as a conventional voltage regulator and reactive power compensator. However, the sizing and siting problem consists of various objectives, such as voltage stability improvement, emission reduction, and cost reduction. It is a great challenge to optimize these contradictory objectives altogether. Hence, to overcome all these struggles, this paper proposes a tri-objective optimization formulation for optimal planning of WDGs and CBs in the power system, considering uncertainties derived from PEVs’ load demand, wind speed, and conventional load. The objectives to be optimized are, namely, voltage stability index, total cost, and greenhouse gas emissions. Moreover, this study assimilates an unaccustomed type of point estimate method (PEM) into strength Pareto multi-objective differential evolution algorithm, which is not only computationally light but also reasonably accurate. Moreover, chance-constrained programming is integrated into the PEM using a maximum entropy method to deal with smooth constraints. The accuracy of the proposed method is evaluated by the Monte-Carlo simulation. Eventually, the proposed algorithm is compared with other standard multi-objective metaheuristic optimization algorithms.

Distribution System Planning with Representation of Uncertainties Based on Interval Analysis

This work presents an approach for optimal distribution system planning (DSP) to minimize the total costs of expansion and operation with the representation of uncertainties in the load demand and in the wind-based distributed generation (WDG). The proposed approach, called interval distribution system planning (I-DSP), is based on an interval power flow (IPF) and the metaheuristic artificial immune system (AIS). The IPF is used to obtain an interval of the total cost that reflects the uncertainties over load and generation. The interval cost is the merit function of the optimization algorithm. The network constraints as the limits of current, voltage and power from substations, in addition to the radiality and connectivity are taken into account. Well-known test systems are used to assess the impact of the uncertainties representation in the DSP problem.

Comprehensive approach for distribution system planning with uncertainties

This study presents a comprehensive approach for the distribution system expansion planning (DSEP) that considers investment, operation, carbon dioxide emission and reliability costs, as well as uncertainties over load demand and wind-based distributed generation. A restoration strategy is taken into account for obtaining the energy not supplied under the ‘N − 1’ criterion and the corresponding reliability cost over a planning horizon. The problem is modelled as mixed-integer non-linear programming by using the artificial immune system algorithm. Also, two methods to represent uncertainties are applied and compared: an interval technique through an interval power flow and a scenario-based approach. Network constraints are considered, as the limits of current, voltage and power from substations, as well as the obtaining of radial and connected topology. The novelty of the proposed interval DSEP consists of handling the uncertainties over operation in an efficient manner through a single step, instead of the several deterministic evaluations of the scenario-based approach. Numerical results are presented for well known test systems, which show the potentials of the proposed approach.

Power Quality Improvement in Stand-Alone SEIG-Based Distributed Generation System Using Lorentzian Norm Adaptive Filter

In this paper, Lorentzian norm based adaptive filter (LAF) is implemented to control voltage source converter (VSC) for improvement in power quality of three phase four wire wind based distributed generation system. This control algorithm operates VSC to control the terminal voltage and frequency in allowable range. In addition to above, neutral current compensation, load balancing, reactive power, and harmonic compensation are also carried out. Therefore, in the system, the generator always supplies the active fundamental component of load current. The LAF is derived from a sliding window type objective function with Lorentzian norm of past errors to overcome bad effect of impulsive environment on system. Hence, it is most suitable filter in wind-based distributed generation system application in order to neutralize the effect of wind impulses on the voltage and frequency of the generator. The entire system comprises induction generator, VSC, and star-delta transformer and nonlinear load is developed in MATLAB Simulink followed by a prototype in the laboratory. The control performance of the said filter is evaluated and validated by simulation and experimental analysis. After performance observation, results are found satisfactory.

Optimal probabilistic based storage planning in tap-changer equipped distribution network including PEVs, capacitor banks and WDGs: A case study for Iran

Due to their cost-effective and environmental-friendly natures, renewable energies as well as Plug-in Electric Vehicles (PEVs) are increasingly utilized nowadays. A critical challenge with renewable energies is natural intermittency and as such can be addressed appropriately using Energy Storage Systems (ESS). In this paper, optimal planning of battery based energy storage units is proposed in distribution network. As an important challenge in optimal storage planning, the uncertainty investigation is dealt with in this work. A new approach which is based on Point Estimate Method (PEM) is introduced as a tool to handle the uncertainty of the load, the Wind-based Distributed Generation (WDG) and PEVs demand, simultaneously. The proposed method is intuitively compared with Monte Carlo Simulation (MCS) as well as conventional PEM for a case study and the results are verified through the comparisons. Moreover, in order to challenge voltage control benefit of the storage units, the under study distribution network is equipped to the tap-changer and the capacitor banks. Whereas the optimal storage planning is a very complicated task, a modified hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS) algorithm is used to solve the related optimization problem. The simulation results for a case study in Iran show the effectiveness of the proposed approach in different scenarios.

Impact of Wind-Based Distributed Generation on Electric Energy in Distribution Systems Embedded With Electric Vehicles

In this paper, the synergy between wind-based distributed generation (DG) and plug-in electric vehicles (PEVs) is studied. MonteCarlo is used to address the uncertainties associated with wind speed variations and charging of PEVs hence simulating their impact at the distribution system (DS) level considering different DG penetration (up to 35%) and different PEV penetration (up to 50%). The excess in active/reactive power, energy exceeding normal (EEN), unserved energy (UE), and energy losses are investigated in this study. Forty-eight penetration scenarios involving DGs and PEVs are studied in this work and simulated in the IEEE 123-bus radial power distribution test system after modeling its secondary circuit in OpenDSS. The results of the simulation show that 30% wind-based DG penetration may be adequate to supply the active energy needed to charge PEVs. However, this might result in a reverse reactive power flow back to the substation.

Optimal Allocation of Wind Turbines Considering Different Costs for Interruption Aiming at Power Loss Reduction and Reliability Improvement Using Imperialistic Competitive Algorithm

In recent years, utilization of wind-based distributed generation (DG) as one of the most widespread used types of green generation technologies has attracted remarkable consideration. Recently, several contributions have accomplished in case of wind-based generation. In this research, in order to minimize the costs of annual energy losses and Energy Not Supplied (ENS) of the distribution networks, a multi-objective probabilistic based approach is proposed to determine the optimal location and capacity of wind-based units along with providing assurance for a desirable level of voltage profile. An Imperialist Competitive Algorithm (ICA) is employed to overcome non-convexity and complexity of the mixed integer optimization problem of DG allocation in the radial distribution networks. For this purpose, a Fuzzy C-Means (FCM) clustering is used to categorize historical data of load demands and output power of DG units. Then, given the fuzzy center point of the clusters and their respective probability generated from FCM clustering implementation, uncertainty consideration is involved to the probabilistic calculation of both of the wind-based DG and load demand. Moreover, during the calculation process of ENS, different costs of interruption are considered for various customers of the network. Finally, presented technique is exerted for an IEEE 33-bus standard test system subject to the system constrains under different cases and effectiveness and capability of the method is assessed. Obtained results demonstrate competence of the method to produce a significant reduction in ENS value and annual energy losses

Adequacy Evaluation of Distribution System Including Wind/Solar DG During Different Modes of Operation

Keen interest in the development and utilization of renewable distributed generation (DG) has been currently observed worldwide. The reliability impact of this highly variable energy source is an important aspect that needs to be assessed as renewable power penetration becomes increasingly significant. Distribution system adequacy assessment including wind-based and solar DG units during different modes of operation is described in this paper. Monte Carlo simulation (MCS) and analytical technique are used in this work with a novel utilization of the clearness index probability density function (pdf) to model the solar irradiance using MCS. The results show that there is no significant difference between the outcomes of the two proposed techniques; however, MCS requires much longer computational time. The effect of islanding appears in the improvement of the loss of load expectation (LOLE) and loss of energy expectation (LOEE).

Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems

Recent development in small renewable/clean generation technologies such as wind turbines, photovoltaic, fuel cells, microturbines and so on has drawn distribution utilities' attention to possible changes in the distribution system infrastructure and policy by deploying distributed generation (DG) in distribution systems. In this study, a methodology has been proposed for optimally allocating wind-based DG units in the distribution system so as to minimise annual energy loss. The methodology is based on generating a probabilistic generation–load model that combines all possible operating conditions of the wind-based DG units and load levels with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer non-linear programming (MINLP), with an objective function for the system's annual energy losses minimise. The constraints include voltage limits at different buses (slack and load buses) of the system, feeder capacity, discrete size of the DG units, maximum investment on each bus, and maximum penetration limit of DG units. This proposed technique is applied to a typical rural distribution system and compared to the traditional planning technique (constant output power of DG units and constant peak load profile).

Supply Adequacy Assessment of Distribution System Including Wind-Based DG During Different Modes of Operation

Keen interest in the development and utilization of wind-based distributed generation (DG) has been currently observed worldwide. The reliability impact of this highly variable energy source is an important aspect that needs to be assessed as wind power penetration becomes increasingly significant. Distribution system adequacy assessment including wind-based DG units during different modes of operation is described in this paper. Monte Carlo simulation (MCS) and analytical technique are used in this work with a new implementation of the islanding mode of operation in the assessment. The results show that there is no significant difference between the outcomes of the two proposed techniques; however, MCS requires much longer computational time. Moreover, the effect of islanding appears in the improvement of the loss of load expectation (LOLE) and loss of energy expectation (LOEE).

The role of taxation policy and incentives in wind-based distributed generation projects viability: Ontario case study

Taxation policy and incentives play a vital role in wind-based distributed generation projects viability. In this paper, a thorough techno-economical evaluation of wind-based distributed generation projects is conducted to investigate the effect of taxes and incentives in the economic viability of investments in this sector. This paper considers the effects of Provincial income taxes, capital cost allowance (CCA), property taxes, and wind power production Federal incentives. The case study is conducted for different wind turbines and wind speed scenarios. Given turbine and wind speed data, the Capacity Factor (CF) of each turbine and wind speed scenario was calculated. Net Present Value (NPV) and Internal Rate of Return (IRR) for different scenarios were then used to assess the project's viability considering Ontario Standard Offer Program (SOP) for wind power.