Grid Distribution System Research Articles

Long term demand forecasting in Jakarta distribution grid system using fuzzy logic and artificial neural network method

Abstract In order to attain equilibrium between energy supply and demand, reliance on conventional methods for precise long-term electricity demand forecasting is no longer viable. The utilization of artificial intelligence, such as fuzzy logic and artificial neural network (ANN) models, emerges as a prospective solution in the current dynamic scenario. This research explores long-term electricity demand forecasting within the Jakarta distribution grid system, employing various fuzzy logic and ANN approaches including Sugeno, Mamdani, Bayesian Regularization, and the Levenberg algorithm. The analysis incorporates time series data spanning 2016 to 2019, encompassing electricity load demand, economic factors, and demographic variables, processed using MATLAB. The outcomes of the four forecasting methods reveal an average error range of 2 to 3%. The findings indicate that employing fuzzy logic and ANN methods for long-term electricity demand forecasting can yield a forecast error of less than 3%. The study recommends future research enhancements through the inclusion of additional time series data and a more refined system.

A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION

The electric vehicle (EV) market is expanding rapidly around the world due to technological advancements, decreasing cost of batteries, and supportive government regulations. It is both a challenge and an opportunity for distribution utilities to manage the additional power demand from EVs. Effective and optimal EV charging scheduling strategies are essential to avoid the adverse effects of large EV penetration in the power grid system. This paper proposes an optimal plug-in electric vehicle (PEV) charging scheduling in a distribution grid system using a hybrid algorithm approach that combines a multiverse optimizer (MVO) and also a barnacle mating optimizer (BMO) termed as HMVO-BMO. The optimization model is developed with the objective to minimize the grid power loss, considering overnight home charging. Random arrival times of PEVs are considered and charging is scheduled based on available power demand on the distribution grid. The proposed methodology is demonstrated on the IEEE 33-bus system with different PEV penetration levels. Comparisons are made between three optimization algorithm approaches, namely the standard MVO and BMO, and the proposed HMVO-BMO algorithms. The simulation results demonstrated that the proposed hybrid technique can achieve better and efficient results in terms of system power loss.

Investigation methods for enhancing the energy consumption capacity of novel PV access distribution networks

Abstract This paper presents a method for reconstructing distribution networks using soft open point (SOP) and demand response (DR) energy storage, which can effectively solve the problems of voltage overruns, line overcurrent, and bidirectional flow currents caused by the photovoltaic (PV) getting into the distribution network. First, the model for the rebuilding of the distribution network is created by using the energy storage method of DR and SOP. Then, MISOCP, which stands for mixed-integer second-order cone programming, is the model that is used to change the distribution network reconfiguration, and the transformed model is solved by the MOSEK algorithm package in MATLAB. Finally, the strategy suggested in this research may successfully enhance the distribution grid’s consumption capacity after PV access, as shown by the verification in the IEEE33 node distribution grid system.

Shared Energy Storage Scheme for Photovoltaic Energy Storage Power Supply System

Abstract While PV-battery hybrid grid integration has been recognized as an effective approach to enhance transient stability, the high operational and maintenance costs of energy storage systems constrain their reliability across various operating scenarios. To overcome this challenge, this paper takes the application of PV-battery subsystems in a distribution grid supply scenario as an example and conducts an in-depth analysis of the transient stability of PV systems during fault conditions. In cases where system demands are not adequately met, this research proposes a solution based on shared energy storage to enhance system supportability while maintaining economic feasibility. By appropriately allocating and sharing energy storage capacity, the system can better respond to sudden load fluctuations and fault conditions, ensuring a stable power supply. Simulation results demonstrate that the proposed shared energy storage solution significantly enhances the transient stability of PV systems during fault conditions, effectively mitigating the destabilizing effects of PV grid integration. Furthermore, from an economic perspective, this solution can reduce energy storage operation and maintenance costs, enhancing overall system sustainability. In conclusion, this study presents a feasible and effective support solution for the application of PV systems in distribution grids, providing valuable insights to advance the reliability and sustainability of clean energy sources.

Low-carbon operation of smart distribution grid based on life cycle assessment and ladder-type carbon trading

A large number of EVs are commonly added to peak loads without dispatch in the smart distribution grid system, and the grid system usually suffers from excessive carbon emission problems. This paper proposes an integrated approach to control the smart distribution grid system by utilizing life cycle assessment and the market mechanism. First, the system’s carbon emissions are estimated according to life cycle assessment and carbon flow theory. Then, a bi-level optimization strategy for a smart distribution grid is presented. The upper-layer operation object is the minimum comprehensive operation cost of the smart distribution grid, which is solved using an optimization algorithm. The lower-level optimization goal is to minimize the operating cost of electric vehicles, which is solved using the CPLEX toolkit. Our simulation results demonstrate that overall operating costs have been reduced by 71.11%, while SDG’s overall carbon emissions have been reduced by 49 kg. The low-carbon growth of the smart distribution grid may be successfully promoted by appropriate trading market scheduling and efficient low-carbon dispatching.

Advancements and Challenges in Integrating Renewable Energy Sources Into Distribution Grid Systems: A Comprehensive Review

Abstract The issues in integrating renewable energy sources (RES) into distribution grid structures are thoroughly examined in this research. It highlights how important this integration is to updating the energy system and attaining environmental goals. The study explores the specific problems confronted by means of on-grid power structures, along with overall performance metrics and compatibility issues. Additionally, it presents a thorough assessment of the attributes of various RES hybrid systems, together with technology from the fields of solar, wind, batteries, and biomass. To be able to spotlight the significance of innovative solutions inside the dispersed technology environment, the integration of RES with combined heat and power system structures is investigated. This study addresses the numerous problems with RES integration into the grid to better comprehend their intricacies. The viability of RES integration is supported by real-world case studies that provide operational examples of dispersed generation systems. The study concludes by discussing the technical, financial, and grid-related problems associated with distributed generating systems' limits and highlighting the contribution of cutting-edge technology and artificial intelligence to their removal. In conclusion, the report highlights the development toward smarter grids and improved distributed generating capacities as the essential component of a robust and sustainable energy future.

Correction to: Performance analysis of smart grid distribution system using global EV charging station as smart load based on automatic variac transformer

Correction to: Performance analysis of smart grid distribution system using global EV charging station as smart load based on automatic variac transformer

Techno-economic evaluation of meshed distribution network planning with load growth and expansion with multiple assets across multiple planning horizons

The smart grid paradigm has ushered in an era where modern distribution systems are expected to be both robust and interconnected in topology. This paper presents a techno-economic-based sustainable planning (TESP) strategy, which can be used as a planning framework for linked distribution systems, seeking to discover a realistic solution among competing criteria of diverse genres. In this comparative analysis-based study, three voltage stability assessment indices—VSA_A, VSA_B, and VSA_W—and a loss minimization condition (LMC)-based framework are used in the initial stage to achieve optimal distributed generation (DG)-based asset optimization for siting, followed by sizing. The respective techniques are evaluated across two variants of multiple load growth horizons spread across 10 years. The suggested TESP technique is tested on two variants of a mesh-configured microgrid (MCMG) with varied load growth scenarios. One variant considers a 65-bus MG with a fixed load growth of 2.7% across two load growth horizons. The other variant considers a 75-bus MG with varied load growth across four load growth horizons, encapsulating an expansion-based planning perspective. The numerical results of the suggested TESP approach in a comparative study demonstrate its effectiveness, and it can be used by researchers and planning engineers as a planning framework for interconnected distribution tools across multiple planning horizons. The proposed study would contribute to enhancing the robustness and interconnectivity of smart grid distribution systems. This dual focus could lead to more cost-effective and reliable power distribution systems.

Analysis of health state before and after fault self-healing based on a high percentage of distributed distribution grid systems

Abstract In this paper, we preprocess the equipment data using by exponential smoothing method, fit the fault information by using the distribution model, and evaluate the fitting degree of the function with the help of the K-S test to derive the equipment fault information function. In accordance with the correlation mapping between the distribution network health index and the existence of faults, the health index of the distribution network equipment in the region before and after the self-healing of faults is comprehensively calculated, and the state assessment of the equipment is carried out. Before and after the fault self-healing of the IEEE33 node system, the system loss is reduced by 58.89%, as evidenced by the results. Before and after the fault self-healing, Condition 1: the line health index ranges from 1.317 to 3.777, and the transformer health index ranges from 2.011 to 4.646. Condition 2: the line health index ranges from 2.284 to 4.059, and the transformer health index ranges from 1.653 to 4.047, respectively. And Condition 3: the health indices all increase by varying from 0.711 to 2.067. Localized equipment degradation has a significant impact on the overall health of the network, with lines affecting the system to a greater extent than transformers. To summarize, the proposed health index state assessment method can effectively determine the health state of distribution networks before and after fault self-healing.

Modeling control strategies for prosumers in a Python-based modular simulation tool

The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses pandapower (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur.

Self-triggered distributed filtering for sensor networks with topology switching via a multi-step ahead approach

In this paper, self-triggered filtering is developed for a class of distributed filtering networks (DFNs) with topology switching. To save unnecessary transmission and computation effort, a self-triggering policy (STP) is developed to avoid monitoring and judgments at every sampling moment, and it simultaneously decides when to get measurements and release the latest estimation to neighbor nodes. Specifically, the STP can predict the next trigger interval according to the local information and the neighbors’ latest data at the triggering moment. However, under STP, a DFN will subject to asynchronous communication due to each filter triggering independently. Moreover, STP and DFN topology switching do contaminate the estimation accuracy. To deal with these challenges, a co-design of STP and distributed filters are developed to achieve good filtering performance with less design conservatism via a multi-step ahead approach. Finally, a numerical example and a smart grid distribution system are presented to demonstrate the effectiveness and applicability of the developed approach.

A Novel Islanding Detection Method for Distributed PV System Based on μPMUs

With the rapid development of distributed PV systems in distribution grids, unintentional islanding is one major concern which maybe cause safety hazards. The existing passive islanding detection methods are mainly based on the voltage and frequency deviations during islanding conditions. Passive methods have relatively large non-detection zone (NDZ) and susceptible to power system faults. Active islanding detection methods applied in inverters have relatively small non-detection zone, but they are usually harmful to power quality. This paper presents a new islanding detection method based on equivalent inter-connection line impedance, which is normally a small value. A measured impedance is calculated with the synchronous phasors from two lPMUs which are installed in a utility substation and a PV system. Under the grid-connected condition, the measured impedance is no more than the equivalent line impedance. While under the islanding condition, the measured impedance is much larger than the equivalent line impedance. So, an islanding condition can be effectively detected by the comparison of the measured impedance with the equivalent line impedance. The non-detection zone of this proposed method is nearly zero. The detection time is normally less than 250ms. This method is applicable for PV systems connected to the power system with a distribution transformer. It is also applicable for a distribution grid with multiple distributed generation systems.

Economic assessment of integrating fast-charging stations and energy communities in grid planning

Distribution grid companies and distribution system operators (DSOs) still mostly follow a traditional framework for grid planning. Such frameworks have so far served DSOs well in the economic assessment and cost–benefit analysis of passive measures, such as grid reinforcement. However, the development towards active distribution grids requires DSOs to also be able to assess an extended set of active measures. To this aim, this paper extends and implements a general planning framework for active distribution grids that builds upon the well-proven traditional framework. The methodology integrated in the framework includes: (1) decoupled models for (i) operation with active measures and (ii) optimal grid investment, and (2) methods for economic assessment considering active measures from both (i) a DSO cost–benefit analysis perspective and (ii) a willingness-to-pay perspective. In this paper, operational models are integrated for two examples of active measures, namely the use of fast-charging stations (FCS) and local energy communities (LEC). The methodology is demonstrated in a long-term grid planning case study for a realistic Norwegian medium voltage distribution system. For this case, grid planning with FCS as an active measure reduces the present value of grid investment costs by 70% compared with a passive grid planning strategy. The results also demonstrate how the methodology can be used in negotiating the price of active measures between the DSO and distribution system actors such as LEC and FCS operators.

Efficient Distributed Energy Resource Voltage Control Using Ensemble Deep Reinforcement Learning

To meet the challenges of low-carbon power generation, distributed energy resources (DERs) such as solar and wind power generators are now widely integrated into the power grid. Because of the autonomous nature of DERs, ensuring properly regulated output voltages of the individual sources to the grid distribution system poses a technical challenge to grid operators. Stochastic, model-free voltage regulation methods such as deep reinforcement learning (DRL) have proven effective in the regulation of DER output voltages; however, deriving an optimal voltage control policy using DRL over a large state space has a large computational time complexity. In this paper, we illustrate a computationally efficient method for deriving an optimal voltage control policy using a parallelized DRL ensemble. Additionally, we illustrate the resiliency of the control ensemble when random noise is introduced by a cyber-adversary.

Optimal sizing and siting of energy storage systems considering curtailable photovoltaic generation in power distribution networks

This work proposes a method for optimal planning (sizing and siting) energy storage systems (ESSs) in power distribution grids while considering the option of curtailing photo-voltaic (PV) generation. More specifically, for a given PV generation capacity to install, this method evaluates whether curtailing PV generation might be more economical than installing ESS. Indeed, while curtailing excess PV generation might be considered a last resort to avoid grid violations during operations, it is typically neglected in the planning phase. The proposed method accounts for the constraints of the power grid (i.e., nodal voltages, lines, and substation transformer limits) modeled by linearized power flow equations to keep the problem formulation tractable. The planning problem minimizes the net investment costs of the ESSs, and the imported and exported electricity costs considering a planning horizon of 20 years. The results are presented for a medium voltage (MV) distribution grid with different levels of installed capacity of PV generation, reflecting future scenarios of PV generation development. The sensitivity of the ESSs’ sizes and investment costs to the electricity prices accounting for variable levels of PV production in the global generation mix is also investigated.

Testing System for PV Grid-Connected Power Condition Systems with Support for Ancillary Services

Due to the high spread of photovoltaic (PV) systems in the low voltage distribution grids there is a substantial number of requirements for the connection of these systems. Therefore, several tests should be performed before the integration of the PV systems into the grid. Moreover, new requirements have been established that are most likely to be implemented in the near future. To provide these tests and verify if PV systems are in accordance with requirements and recommendations, a testing setup for the PV systems was developed. This testing system consists of a controllable power source prepared to receive energy and to inject it into the grid. In fact, that system imposes a controllable voltage, in amplitude and frequency, to the PV system to simulate the perturbations of the grid. Since the system under test must inject energy in accordance with the standards’ specifications, then the controllable power source that emulates the grid must receive that energy. Moreover, it should also be prepared for PV systems that are able to provide ancillary services, including new ones that support imbalanced networks. A fast and robust control system will be used for this controllable power source. Several experimental tests from the developed prototype are presented.

Bi-Level Multi-Objective Planning Model of Solar PV - Battery Storage-Based Ders in Smart Grid Distribution System

The Bi-level Multi-Objective Planning Model of Solar PV-Battery Storage-Based DERS in Smart Grid Distribution System is a research paper that proposes a planning model for the implementation of distributed energy resources (DERs) in a smart grid distribution system. The model is designed to optimize the deployment of solar PV and battery storage systems in the grid, while taking into account various technical, economic, and environmental factors. The proposed planning model is based on a bi-level multi-objective optimization approach, which considers both the objectives of the utility and the objectives of the DER owners. The upper-level objective is to minimize the total cost of energy supply to the grid, while the lower-level objective is to maximize the revenue of the DER owners. The model is implemented using a genetic algorithm, which is used to search for the optimal solution.
 The model is also capable of considering the uncertainties associated with solar PV and battery storage systems, such as weather conditions and battery degradation. The results of the study show that the proposed planning model can effectively optimize the deployment of solar PV and battery storage systems in a smart grid distribution system. The model is also shown to be robust to various uncertainties associated with DERs, such as weather conditions and battery degradation. Overall, the proposed planning model provides a valuable tool for the implementation of distributed energy resources in a smart grid distribution system. By optimizing the deployment of DERs, the model can help to reduce the cost of energy supply, while also improving the reliability and environmental performance of the grid.
 This project describes the multi objective battery sizing and storage system for grid connected system using renewable energy solar and wind system.

An Improvement of Stability in Distribution Network by Integrated Photovoltaic-Electric Spring System

The electricity grid's distribution system is a crucial component. Power quality issues in the distribution system have received a lot of attention in recent years due to the extensive integration of variable renewable energy generation. Modern digital grid technology, electric spring (ES), has previously been utilised to stabilise voltage and power in a system that relies heavily on unregulated or completely renewable energy sources. To control voltage and power, it has been recommended as a demand-side strategic approach. It has been suggested that electric springs (ES) might be used as a request technology to boost the reliability and efficiency of future power grids that rely heavily on intermittent renewable energies. At now, ES is mostly used for controlling grid voltage and service frequency. This study describes a method for controlling and balancing power in a newly integrated ES and PV system configuration, and it examines how this method may be used to achieve responsive supply equilibrium in electricity distribution networks. The suggested system allows for the maximum amount of collected PV power to be sent to the grid via the ES, while also actively regulating the energy usage of its ES-associated smart load in order to maintain a constant supply-demand power balance. Because the smart-load power associated with the ES follows a suitable usage patterns to adjust for possible forecast failures of the PV power output, battery storage is unnecessary in the suggested architecture. Excellent performance of the suggested design method is demonstrated using Matlab/Simulink simulations.

A Novel Hybrid Polygeneration System Based on Biomass, Wind and Solar Energy for Micro-Scale Isolated Communities

The availability of freshwater and energy is a serious issue in remote and islanded areas, especially at a small scale, where there may not be the possibility to access the grid and/or water distribution systems. In this context, polygeneration systems operating on the basis of local, renewable energy sources can be an answer to the users’ demand for electricity, heating, cooling, and domestic hot water. The scope of the proposed paper was to investigate, numerically, the energy and economic feasibility of a novel hybrid polygeneration system powered by biomass, solar, and wind energy for a micro-district of households. The proposed system consists of a biomass-fueled steam cycle, wind turbine, photovoltaic field coupled with thermal and electrical energy storage, adsorption chiller, and a reverse osmosis water desalination unit. The system is also assisted by an LPG generator set running as backup. The system provides space heating and cooling, electrical energy, and fresh and domestic hot water to 10 households located on Pantelleria Island, Italy. The proposed system is modelled and simulated through TRNSYS software with realistic user demand. The energy and economic performance of the proposed system are assessed with respect to a reference system in different scenarios, taking into account islanded operation, connection to the grid, and biomass tariffs. The results show that the proposed system achieves an excellent primary energy saving performance in all the investigated scenarios, with savings of more than 94% for all the investigated scenarios. Excluding any kind of funding, in case of new investment for the system, the simple payback oscillates between 7 and 12 years, showing that the developed alternative is fairly valid with respect to traditional solutions.

Concepts, Configurations, and Challenges of Solid-State Transformer: A Review

Abstract: Rapid and suddenly increasing use of small distributed renewable energy resources (DRER) and distributed energy storage (DES), reduced compact dynamic electrical power consumer-like electrical vehicle loads, the requirement of bidirectional power flow communication network to exchange critical information, other ancillary services, etc. has to be envisaged and fulfilled by a future smart distribution grid system. A solid-state transformer (SST) has more potential solutions to provide stable and efficient distribution system operation to overcome the above problem.From various designs of converter control schemes, the solid-state transformer concept has been evaluated and summarised. The benefits and downsides of converters in the AC/DC, DC/DC, and DC/AC stages are studied for the best configuration. The High Frequency Transformer(HFT) is a main part of SST.The design and optimization of a HFT is approached on the objective of minimising the weight and size of the SST.Researchers also face several challanges in the prototype design and implementation of SST before operating effectively in the distribution system which is presented . Some of expected solutions and future recommendations for the establishment of the solid state transformer for future smart electrical distribution systems are discussed.