Model Of Distributed Generation Research Articles

Research on assessment method of maximum distributed generation hosting capacity in distribution system with high shares of renewables and power electronics.

With the rapid development of distributed generation (DG) within the framework of modern power systems, accurately assessing the maximum DG hosting capacity in distribution networks is crucial for ensuring the safe and stable operation of the power grid. This paper first introduces an assessment model of maximum DG hosting capacity in distribution network based on optimal power flow (OPF). Then, a two-step method that combines the linearization method and the recursive method is proposed, which consists of two parts: firstly, using linearization method to quickly calculate the preliminary assessment value of maximum DG hosting capacity, and then using a recursive method to accurately correct the preliminary assessment value. Additionally, the proposed improved comprehensive sensitivity index and safety constraint verification method can enhance the computational efficiency and accuracy of the recursive algorithm. Finally, the proposed methods were simulated and validated on the IEEE 33-bus system.

EXPERIMENTAL ASSESSMENT OF CONSUMER UNBALANCE AND NONLINEARITY EFFECTS IN THREE-PHASE NETWORKS WITHOUT NEUTRAL CONDUCTOR

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

THE EFFECTS OF HYBRID SLIDING MODE LEARNING CONTROL AND FEEDFORWARD ANGLE DROOP CONTROLLER WITH HIGH DROOP GAIN IN HYBRID MICROGRID WITH LOAD UNCERTAINTY AND NONLINEARITY

The present study proposes a feedforward angle droop strategy based on a discrete-time mathematical model for operation dispatchable distributed generation (DG) units connected directly or through voltage source converters (VSC) to the microgrid. These units should supply their local and common loads. Droop coefficients should be increased to improve power division between different DG units. Also, a time delay in systems’ state variables and controller input is a disruptive parameter for the control system's performance. These two parameters have negative effects on network stability. To ensure the stability of the closed-loop system, a predictive sliding mode controller can be used based on discrete-time systems. However, this strategy cannot reduce the chattering phenomenon. This paper discusses the effect of time delay and increases in droop angle coefficients on the whole system and how these impacts can be eliminated. So, a combination of integral sliding mode controller (ISMC), composite nonlinear feedback (CNF), and sliding mode learning control (SMC), which is called hybrid SMC, is used with an angle droop controller.

Macroeconomic, energy, and emission effects of solar PV deployment at utility and distributed scales in Saudi Arabia

For the first time, this paper assesses the macroeconomic, energy, and emission impacts of solar photovoltaic (PV) deployment in Saudi Arabia by linking an energy-and-environment-sector augmented macroeconometric model with a power model and a distributed generation model while distinguishing between utility scale and distributed-generation scale of the PV deployment. We analyze three policy scenarios of deploying 5.5 TWh PV for the period 2021–2030: (S1) fully government-funded utility-scale PV deployment, (S2) half-government-funded utility-scale PV deployment, and (S3) household-funded distributed generation scale PV deployment with little government support. We find that: S1 is best suited if the objective is fiscal expansion, 2 is best suited to improve non-oil fiscal positions, increase non-oil value added and employment, and increase household consumption, and S3 is best suited to reduce energy consumption and associated emissions and enhance international trade. We show that government and foreign investment play an important role, and it would be desirable for the Saudi government to partner in financing renewable energy projects, saving budgetary resources to invest in other projects to support economic growth.

Optimal Planning of User-side Scaled Distributed Generation Based on Stackelberg Game

BACKGROUND: User-side distributed generation represented by distributed photovoltaic and distributed wind turbine has shown an expansion trend of decentralized construction and disordered access, which is difficult to satisfy the demand for large-scale exploitation and sustainable development of distributed generation under the low-carbon transformation vision of the power system.
 OBJECTIVES: To address the interest conflict and operation security problems caused by scaled distributed generation accessing the distribution network, this paper proposes the optimal planning method of user-side scaled distributed generation based on the Stackelberg game.
 METHODS: Firstly, a cluster planning and operation mode of distributed generation is established. Then, a prediction method for planning behavior of user-side distributed generation is proposed in order to predict whether users will adopt the self-build mode or the leasing site mode for distributed generation. Finally, in order to reveal the game relationship between the distribution network operator and the users in the allocation of distributed generation resources, a bi-level planning model for scaled distributed generation is established based on the Stackelberg game.
 RESULTS: The simulation results show that the revenue of the distribution network operator under the gaming model increases by 10.15% and 16.88% compared to the models of all users self-build distributed generation and all users leasing distributed generation site, respectively, while at the same time, individual users also realize different degrees of revenue increase.
 CONCLUSION: The case analysis validates the effectiveness of the proposed method in guiding the rational and efficient planning of user-side distributed generation.

Optimal Allocation Stochastic Model of Distributed Generation Considering Demand Response

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model.

A Low-Order Steady-State Model of Electric Springs for Conservation Voltage Reduction in Active Distribution Networks With Renewables

Electric spring (ES), as a new type of power electronic device, can flexibly regulate the voltage of noncritical load (NL) and improve the voltage quality of critical load (CL). It can further increase the energy savings of conservation voltage reduction (CVR) in active distribution networks (ADNs). However, the high-order nonlinearity of ESs steady-state model brings the difficulty of solving the CVR optimization problem Therefore, this paper proposes a low-order steady-state model of ESs for CVR in ADNs. The operation modes of ESs are extended based on analysis of voltage regulation characteristics, firstly. The equivalent circuit of the bus with an ES is also derived, and a steady-state model with low-order nonlinearity is further proposed. Then, considering significant difference in response speeds among ESs, distributed generation (DGs) and discrete control devices (DCDs), a two-stage coordinated operation model of ESs, DGs and DCDs is established. By means of convex envelope and linearization, the nonconvex nonlinear constraints are transformed into linear constraints, which can improve the convergence and solving efficiency of the optimization model. Finally, the case studies demonstrate that ESs can increase the CVR energy savings, and the proposed low-order steady-state model can improve the solving efficiency of this CVR optimization problem.

Research of Islanding Operation and Fault Recovery Strategies of Distribution Network Considering Uncertainty of New Energy

With the problems of fault handling in the distribution network, few studies concern the correlation between islanding operation and fault recovery. Thus, this paper proposes an islanding operation and fault recovery strategy for the distribution network considering the uncertainty of new energy. Firstly, the objectives of the distribution network islanding division scheme and operation optimization are established. Combined with distribution network radiation constraints, islanding power supply capacity, safety constraints, and distributed generator (DG) operation constraints, a rolling optimization method is used to construct the distribution network islanding division and operation model. Secondly, in the fault recovery stage, considering the characteristics of the island operation stage, a node load weight value is designed. A distribution network fault recovery model is then constructed with the goals of ensuring greater load power supply recovery, improving electricity satisfaction, and reducing network losses and switching times. Thirdly, considering the randomness of intermittent DGs such as wind power and photovoltaics, an uncertainty model of intermittent DGs is constructed. A solution method for the distribution network islanding operation and fault recovery model considering uncertainty is proposed by combining scenario generation reduction methods and second-order cone programming theory. Finally, the proposed method’s feasibility and effectiveness are verified using the improved IEEE 33-node distribution network. The results show that in the islanding division stage, the node voltage consistently remains between 1.08 pu and 1.1 pu when new energy achieves up to 34.09% and 48.65%, and the line losses represent approximately 0.22% to 0.26% of the total load when the initial energy levels of the storage are at 50% and 80%. In the fault recovery stage, compared to the method without network reconstruction, the system shows significant power loss reductions of approximately 11.9%, 13.6%, and 14.2% in three respective cases.

Analysis of an improved three-phase voltage source converter with high-performance operation under unbalanced conditions

The power system is rapidly transforming into a new large-scale distributed power generation model using renewable energy. Grid-connected converters, particularly voltage control compensators like STATCOM, will be crucial to the functioning of this new situation. Voltage source converter (VSC) is one of the most commonly used topologies in grid connection converters. As a distributed power-generating device, VSCs can be utilized as a means of connecting sustainable energy sources to the power grid. The three-phase voltage source currents or voltage ripples may appear in the DC chain. This study examines the enhanced three-phase VSC, pinpoints the factors that contribute to the great performance of VSCs, and looks at future trends in three-phase VSCs. VSC is a power electronic device capable of converting DC voltages to three-phase AC voltages and is commonly used in areas such as grid connection, reactive power compensation and harmonic suppression. In practical applications, various types of grid or load disturbances pose hidden dangers to the normal operation of the VSC. Due to the presence of unbalanced grids and loads, significant double frequency.

Optimal Power Flow in Distribution Network: A Review on Problem Formulation and Optimization Methods

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the “Dual-Carbon” target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations.

A robust transient and sustainable faults location approach for AC microgrid based on voltage and current difference measurements

The most challenging problem to protect the microgrids is the integration of different types of distributed generations, which leads to the bidirectional load flow and different fault current levels. On the other hand, the use of voltage and current measurements in nodes increases the cost of fault location methods. This paper proposes a technique to calculate the distance and branch of fault for microgrids in islanded and grid-connected modes by calculating the current and voltage difference at the terminals of each branch. This method obtains the voltages and currents of all nodes by installing measuring devices at the point of common coupling and the location of distributed generations. This technique uses a distributed parameter line model and node data to determine fault locations in the microgrid, considering distributed generation such as wind turbines, photovoltaic systems, fuel cells, and electric vehicles. This technique does not require distributed generation models. Moreover, it requires less than a quarter of the data of a cycle. The efficiency of proposed the technique is investigated by a modified IEEE 15-bus in MATLAB/SIMULINK and a 7-bus test system in the power system simulator. Besides, studies are conducted on the impacts of various fault resistance and inductance, different fault locations, different fault inception angles, measurements error, and ARC faults. The results showed that the proposed approach is extremely accurate compared to other fault location methods in microgrids.

Research on Optimal Configuration of Distributed Generation based on Multi-scenario Analysis

In order to make the grid-connected planning of distributed generation more reasonable, the uncertainties of intermittent distributed generation output and load forecasting are included in the solution process. Firstly, multi-scenario analysis is introduced to transform the source load uncertainty problem into deterministic problem, and the Latin super-force sampling method is used to generate the initial planning scene. The density peak clustering idea and elbow method are used to improve the K-means clustering algorithm and reduce the scene. Secondly, the optimal allocation model of grid-connected distributed generation is constructed with the minimum annual comprehensive cost as the objective function. Finally, in view of the slow convergence speed and easy to fall into local optimum of particle swarm optimization (PSO), an adaptive inertia weight factor is adopted to improve PSO, and the effectiveness of the proposed model and method is verified by IEEE 33-node standard simulation example.

Contingency constrained TCSC and DG coordination in an integrated transmission and distribution network: A multi-objective approach

Transmission System Operators (TSO) deploy Flexible AC Transmission Systems (FACTS) for congestion management in order to meet technical and multilateral power supply transactions. These power commitments are however constrained by thermal, voltage and stability limitations. On the other hand, the campaign to de-carbonize the power supply framework as seen Distribution System Operators (DSO) accommodate an increased penetration of Distributed Generation (DG) within their distribution networks. However, prerogative of system operators for separate planning of FACTS and DG systems can worsen power system’s key performance indicators especially under huge load growth and severe contingencies. Therefore, this paper developed an approach for contingency constrained coordination of Thyristor Controlled Series Compensator (TCSC) and DG through a multi-level optimization, comprising of a hybrid real power flow index and particle swarm in the first level and a multi-objective variant of particle swarm optimization in the second level. The contingency constrained coordination aimed to improve Available Transfer Capability (ATC), power loss and voltage deviation. Two models of DG were coordinated with TCSC under normal and contingency cases. Results indicate that while ATC improvement for various transactions were achieved with TCSC, additional power losses incurred was further reduced with DG deployment in coordination with TCSC. Furthermore, the Pareto front, which establishes the correlation between objectives shows a diving parabola that is partly nonlinear. Also, the TCSC−DGPQ provide superior ATC and power losses compared with TCSC−DGPV. Again, under (N−1) contingencies, the TCSC−DGPQ provides improved ATC compared with other contingency cases under TCSC only.

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

Compensation of Distributed Generations Outage Using Controlled Switched Capacitors

Researchers recently came up with innovative ways to generate enough electricity to meet the rising demand through establishing an electricity distribution system and enhancing power quality on the customer side. One of these innovative ways is the installation of distributed generation (DG). DG is widely used in modern networks due to its great benefits of improving the voltage profile and the system’s power quality. Additionally, DGs are ideally placed near the end user in distribution systems to improve the system’s performance while minimizing power losses and enhancing voltage profile in the grid. DG recently grew in importance, and its penetration increased in most distribution systems. Due to the spreading of DG in the power system, the dynamic performance of the system is affected. This paper studies the system’s performance and behavior under condition of DG outage from the system. The model of DGs in this study assumes two cases of the power provided to the network; the first case considered DG units injecting active power only to the grid (unity power factor). In the second case, DG injects both active and reactive power to the system. After that, outage of DG units is fixed by injecting a reactive power source using a capacitor with a controlled switch to compensate the outage of DGs from the distribution system and to reduce the outage’s negative effect on the network. The sizing of capacitors is optimized using a harmony search algorithm (HSA) in the same location of the DG units.

General Modelling Method for the Active Distribution Network with Multiple Types of Renewable Distributed Generations

With a proliferation of diverse types of renewable distributed generation (DG) into the distribution network, an equivalent model of an active distribution network (ADN) is extremely important, since the detailed modeling of the whole ADN is much more complex and time consuming. However, different studies developed different model structures of ADNs, which are difficult to be applied in a power system simulation. At the same time, the DG’s low voltage ride through the (LVRT) control was not considered in the existing ADN model, which may lead to a large modelling error. In this paper, a general equivalent model is developed for the ADN with a significant amount of DGs, based on a two-step modelling method. Step one, motivated by the dynamic similarities between the doubly-fed induction generator (DFIG)-based wind turbines, direct drive permanent magnet synchronous generator (DDPMSG)-based wind turbines, and photovoltaic (PV) generation, a general model structure of a renewable DG is initially developed. Then, an aggregation method for the DG’s nonlinear subsystems of the low voltage ride through (LVRT) control and the converter’s current limits are presented. Step two, the ADN model is represented by a general renewable DG model paralleled with a composite load model, and the model is validated, based on an actual distribution network with different renewable DG penetrations and different disturbance degrees. The simulation results show that our model outperforms others with acceptable errors.

Quantifying the uncertainty imposed by inaccurate modeling of active distribution grids

Replacing conventional generation with inverter-interfaced units has turned distribution networks (DNs) from consumers to active and responding intelligent DNs. These modern DNs contain several devices that can support the transmission network (TN) and system stability. Typically, deterministic and aggregated models for inverter-interfaced generation and conventional loads are used to include entire DNs in bulk system stability studies, and contributions from smart loads are neglected. This approach introduces errors to the dynamic modeling that can lead to instabilities. In this paper, we first present a full detailed model of a modern DN, enhancing existing thermal load and distributed generation models to include frequency and voltage support and protection functions required in low-inertia systems. Then, we incorporate the uncertainty that stems from the parameterization of such units using a Monte-Carlo method. Finally, we assess the impact of neglecting specific protection and support functions against frequency disturbances. The results show the crucial importance of accurately modeling protection and support functions to analyze the impact of modern DNs on bulk system stability. In addition, the findings highlight the increased relevance of considering uncertainty in stability studies of weak and low-inertia power systems.

Technical-economic viability of tubular biodigester and photovoltaic energy for rural property

Dairy cattle farming is an activity of great economic importance for Brazil, and the state of Parana has a relevant participation in national milk production. In order to reduce production costs, the generation of the electric energy in the distributed generation model appears as an alternative for the milk cattle farmer. Therefore, there are two possible options: The use of the property's animal waste for turning biomass into a biogas source, or the installation of photovoltaic panels. The objective of this work was to evaluate and compare through the use of economic engineering tools such as NPVand Payback, the feasibility of implementing these energy sources in a milk-producing property. The results demonstrated the economic viability of both forms of energy generation for a 25-year project horizon, considering a minimum attractiveness rate of 6,5%. Among the scenarios evaluated, the most financially interesting alternatives were those in which the cost of implementing the system was financed, since the considered interest rate of 3% per year is lower than the average inflation of 5,92% per year on the investment balances.

Optimal Allocation Method of Distributed Generation in Active Distribution Network Based on Bi-level Programming

Abstract The optimal allocation method to distributed generation(DG) of active distribution network was studied. Firstly, a bi-level programming model of DG in active distribution network is established. For the cause of determining the optimal installation location and capacity of DG, the upper layer model considers the minimum cost of DG investors as the objective, and the decision variables are the installation location and capacity of DG. The lower layer model considers the system operation with the objective of minimizing the network loss. The decision variables include the voltage amplitude and phase angle of each node and the consumption of distributed generation. Secondly, an modified grey wolf algorithm is proposed to solve the lower layer model, and the lower layer model is turned to a second-order cone programming problem by convex relaxation and linearization technology. Finally, the bi-level programming model developed in this paper is verified by taking IEEE33 bus system as an example, and the simulation results verify the effectiveness of the proposed method.

Coordinated planning of distributed generation and soft open points in active distribution network based on complete information dynamic game

Currently, the lack of regulation ability in distribution systems causes severe limitations in the wide grid connection of high-penetration renewable distributed generation (DG). By improving the access of SOPs, it will considerably improve the economy, flexibility, and controllability of distribution system operation. A bi-level coordinated planning model of DG and soft open points (SOPs) in an active distribution network is proposed based on a complete information dynamic game to coordinate the interests and demands of DG investors, distribution companies, and electricity consumers, and flexibly adjust the power between feeders. The model's planning stage determines the sequential decision relationship from the perspective of improving the consumption of DG and providing economic value. It analyzes the dynamic game behavior and builds the planning model to determine the optimal installation sites and capacities of DG and SOPs for different stakeholders. At the optimization stage, the operation model of the distribution network is established to reduce the network loss and voltage deviation and balance the feeder load. A hybrid optimization method is used to solve the operation-planning model using the iterative search method and mixed-integer second-order cone programming. Furthermore, numerous simulations and comparisons are carried out on IEEE 33-node system, and the results show that the proposed method can not only meet the balance of interests of different stakeholders, improve the feasibility and economy of planning schemes, but also comprehensively improve the operation state of distribution system.