Power System Configuration Research Articles

ENHANCING VOLTAGE STABILITY AND ENERGY OUTPUT OF PLTM SALIDO IN THE PLN DISTRIBUTION NETWORK

Distributed generation (DG) integration in PLN’s extended distribution network often encounters voltage instability issues due to high voltage drops. These challenges restrict power delivery and can lead to disconnection, particularly during peak load conditions. This study investigates methods to enhance voltage stability and optimize the energy output of PLTM Salido. Using ETAP Power Station software, various operational schemes were simulated to address the problem of low terminal voltage. The study focused on the impact of reactive power injection and governor settings optimization to maintain generator performance under high demand. Results indicate that reactive power injection significantly improves terminal voltage, ensuring continuous power delivery. Adding a third generator unit further increased energy supply capacity, leveraging the full potential of available hydropower. The optimized operation of PLTM Salido with two generators increased daily energy output by 32.2%, while introducing a third generator boosted energy delivery by 107%. This research highlights the critical role of reactive power management and system configuration in enhancing the performance of small-scale hydropower plants in rural distribution networks.

Charging Optimization with an Improved Dynamic Programming for Electro-Gasoline Hybrid Powered Compound-Wing Unmanned Aerial Vehicle

For a longer endurance of vertical and level cruise flight, an electro-gasoline hybrid power system is introduced on a compound-wing unmanned aerial vehicle (UAV). After discharging during vertical flight, the battery pack is charged by a piston engine-driven generator, which simultaneously powers the UAV for level cruise flight. A charging model is established based on the configuration of the hybrid power system. Considering fuel consumption and battery attenuation within the typical flight profile of a compound-wing UAV, an optimized charging plan is developed using dynamic programming to determine the trajectory of the generated power sequence. To address deviations between ideal and practical flight conditions in terms of charging performance, a feedforward compensation is introduced to improve optimal tracking control within the dynamic programming framework. Simulations validate the effectiveness of the optimized charging plan, while testbench experiments confirm improvements achieved through compensation enhancement. The results demonstrate practicality with minimal overall cost compared to other conventional control plans.

The Effect of Adding Generation Bus to the Short Circuit Current Level in Electrical Power System

The need for electrical energy is changing and increasing, requiring system flexibility always to be changed and developed. On the other hand, changes in the configuration of the electric power system due to the addition of components or sub-systems also change system parameters such as impedance or level of fault current (short circuit current). This paper presents a study of changes in the fault current caused by adding a new generating bus to an electric power system. This paper aims to determine the change in short-circuit current due to the addition of generators on new buses connected to existing buses to identify the most suitable location for adding these generators based on short-circuit current levels. Three-phase short circuit faults are simulated and analyzed on the IEEE 14 bus standard system using ETAP software with a base MVA 615 MVA. The addition of generators was carried out at five locations with varying generating capacities. The research results show that the short circuit current will change significantly when adding a generator bus to bus 13, with an average percentage change of 5,656%. The smallest change occurs when adding a generator to bus 2, with an average percentage change of 2,417%. The research results conclude that connecting a generator to a new bus linked to an existing generator bus (PV bus) is more effective than connecting it to a new bus associated with a load bus (PQ bus).

Cost-effectiveness and reliability evaluation of hydrogen storage-based hybrid energy systems for unreliable grid

A critical issue regarding the unreliable electricity supply in regions experiencing frequent grid outages poses significant economic and social challenges. Despite the integration of renewable energy sources like photovoltaic (PV) systems, the intermittent nature and low reliability of these resources necessitate additional energy storage solutions. The study investigates the effectiveness of various power system configurations, including PV only, PV/BES, and PV/BES/H2 systems. Using HOMER software, the study delves into investigating the impact of different outage parameters, specifically focusing on the outage durations and frequencies to the reliability and cost-effectiveness of these systems. The study analyzes how these outage parameters influence the loss of power supply probability (LPSP) and the cost of energy (COE). Three cases were being investigated in this study, which are Case 1: Varying mean outage duration (MOD) with fixed outage frequency (OF), Case 2: Varying OF with MOD and Case 3: Varying both the MOD and OF. The inclusion of H2 storage significantly reduced the LPSP in Case 1, from a range of 0.882%–2.79% in the PV/BES system to a much lower range of 0.15%–0.392%. In Case 2, the PV/BES/H2 system also markedly improved reliability, lowering the LPSP from 0.0751% to 1.28% in the PV/BES system to just 0.0279%–0.189%. The results of Case 3 demonstrate that OF has a greater impact on system reliability, as evidenced by a significantly larger rate of change in LPSP when varying OF with constant MOD compared to varying MOD with constant OF. Therefore, the inclusion of energy storage significantly enhances reliability, with the PV/BES/H2 system showing the lowest LPSP values in both cases. However, COE for the PV/BES/H2 system was higher in both cases, ranging from 0.22 to 0.326 $/kWh, compared to 0.101 to 0.156 $/kWh for the PV/BES system. This highlights the need for advancements in H2 storage technology to reduce cost. These findings underscore the critical importance of accurately sizing components to ensure a reliable and economical power supply in regions with unstable grids.

Optimal Battery Storage Configuration for High-Proportion Renewable Power Systems Considering Minimum Inertia Requirements

With the continuous development of renewable energy worldwide, the issue of frequency stability in power systems has become increasingly serious. Enhancing the inertia level of power systems by configuring battery storage to provide virtual inertia has garnered significant research attention in academia. However, addressing the non-linear characteristics of frequency stability constraints, which complicate model solving, and managing the uncertainties associated with renewable energy and load, are the main challenges in planning energy storage for high-proportion renewable power systems. In this context, this paper proposes a battery storage configuration model for high-proportion renewable power systems that considers minimum inertia requirements and the uncertainties of wind and solar power. First, frequency stability constraints are transformed into minimum inertia constraints, primarily considering the rate of change of frequency (ROCOF) and nadir frequency (NF) indicators during the transformation process. Second, using historical wind and solar data, a time-series probability scenario set is constructed through clustering methods to model the uncertainties of wind and solar power. A stochastic optimization method is then adopted to establish a mixed-integer linear programming (MILP) model for the battery storage configuration of high-proportion renewable power systems, considering minimum inertia requirements and wind-solar uncertainties. Finally, through a modified IEEE-39 bus system, it was verified that the proposed method is more economical in addressing frequency stability issues in power systems with a high proportion of renewable energy compared to traditional scheduling methods.

Advanced AI and renewable energy sources for unified rotor angle stability control

Maintaining a reliable electricity supply amidst the integration of diverse energy sources necessitates optimizing the stability of power systems. This paper introduces a groundbreaking method to enhance the efficiency and resilience of power grids. The increasing dependence on renewable energy sources poses significant challenges to traditional power networks, thereby demanding innovative solutions to uphold their stability and security. To address these challenges, we propose an architecture that seamlessly unifies dynamic, transient, and static rotor angle stability (RAS) controls into a single, streamlined system. Utilizing reinforcement learning and real-time decision-making, we present Lazy Deep Q Networks (LDQNs) as a novel approach to RAS control. LDQNs provide real-time rotor angle instructions to RAS devices, enabling precise and efficient stability management. The incorporation of mass-distributed energy storage further augments the system's responsiveness and flexibility, mitigating fluctuations and promoting overall stability. This study advances the application of AI methods to RAS control, building on prior research in frequency and voltage stability frameworks. The proposed system outperforms conventional RAS control methods by integrating LDQNs with mass-distributed energy storage, offering superior performance and adaptability. Case studies validate the effectiveness of the unified RAS framework, demonstrating its advantages over traditional approaches across various power system configurations.

Ethereum blockchain-based hierarchical cooperative power sharing in a group of microgrids

ABSTRACT Cooperative power-sharing (CPS) is one of the cutting-edge power management strategies the power system planners contemplate transforming from its traditional structure to a more decentralized framework as the globe prepares for a future with low carbon emissions. Cooperative microgrids are considered one of the future power system configurations where individual microgrids (MGs) can support each other through CPS. This paper introduces a decentralized energy market (DEM) to operate CPS hierarchically between MGs, groups of MGs, and the utility grid. The security of power-sharing transactions and the privacy of MGs pose challenges in terms of confidentiality and data integrity. To deal with these issues, a DEM framework based on distributed ledger technology (DLT) is proposed to ensure power transactions between cooperative MGs with a guarantee. A scheme is built on the Ethereum testnet to estimate the performance and validation of the proposed smart contracts. A case study with actual data for a group of four MGs is presented to validate the effectiveness of the proposed framework. The proposed blockchain approach results in money transactions resulting in MG1 receiving $485; while MG2 gets $525, MG3 and MG4 generates a revenue of $435 and $362.5 respectively. The numerical results demonstrate that MGs can actively share power with peers and achieve economic benefits.

Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm

INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution.
 OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs
 METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm.
 RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks.
 CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.

Study on Primary Frequency Regulation Reserve Configuration of High Wind Power System Considering Governor Limiting Links

With the large-scale use of wind turbines, the ability of the power system to resist frequency fluctuations has been greatly weakened, making the contradiction between frequency regulation supply and demand in the power system increasingly prominent. In order to ensure the frequency security of the power system, this paper conducts research on the primary frequency regulation (PR) backup configuration problem of a power system containing a high proportion of wind power. First, according to the dynamic properties of the speed regulator, a system frequency response model considering the limiting link is established. And the system frequency response model is transformed into a time-domain analytical function of PR reserve capacity and frequency maximum value. On this basis, a PR backup configuration model of a power system containing a high proportion of wind power is constructed with the optimization goal of minimizing system operating costs and taking into account the limiting link constraints. It is proposed to use the L-shape algorithm to decompose the model into main problems and sub-problems, which effectively reduces the solution complexity of the model. Finally, the correctness and effectiveness of this model are verified based on the improved IEEE 39-bus system.

Minimize Total Cost and Maximize Total Profit for Power Systems with Pumped Storage Hydro and Renewable Power Plants Using Improved Self-Organizing Migration Algorithm

This study presents the application of an improved self-organizing migration algorithm (ISOMA) for minimizing the total electricity production expenditure (TEPE) and maximizing the total electricity sale profit (TPRF) for hydrothermal power systems (HTPS) without and with renewable energies. Two power system configurations were employed to test the real efficiency of ISOMA while dealing with two objective functions. In the first configuration, there was one thermal power plant and one hydropower plant, while in the second configuration, wind and solar energy were both connected to the first system. The results achieved in the first configuration with the first objective function indicated that ISOMA not only outperformed SOMA according to all comparison criteria but was also superior to other methods such as evolutionary programming (EP), acceleration factor-based particle swarm optimization (AFPSO), and accelerated particle swarm optimization (APSO). The evaluation of the results achieved by ISOMA in the second configuration with the objective function of maximizing the TPRF revealed that ISOMA could reach better profits than SOMA in terms of maximum, mean and minimum TPRF values over fifty trial runs. As a result, it was concluded that pumped storage hydropower plants are very useful in integrating with renewable power plants to cut total cost for thermal power plants and in reaching the highest profit for the whole system. Also, ISOMA is a suitable algorithm for the considered problem.

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

Optimal design and analyzing the techno-economic-environmental viability for different configurations of an autonomous hybrid power system

In the present day, there is widespread acceptance of autonomous hybrid power systems (AHPSs) that rely on renewable energy sources (RESs), owing to their minimal adverse effects on the environment. This paper evaluates and compares three various AHPS configurations comprising photovoltaic (PV) modules, wind turbines (WTs), batteries, and diesel generators (DGs), using a recent optimization approach. A new optimizer 'Dandelion-Optimizer' (DO) is applied to tackle design problems. Real-time meteorological data from Siwa Oasis in northwest Egypt was utilized to determine an optimum design of system components for the purpose of providing sustainable power to this remote region. The system configurations are effectively modelled and optimized to achieve the minimum cost of energy (COE), while also minimizing the loss of power supply probability (LPSP) and carbon dioxide (CO2) emissions. As per the results, the last configuration (PV with both backup equipment) is the most optimal one in terms of the lowest cost, whereas the first configuration (PV and WT with both types of backup equipment) is the most optimal one with regards to the lowest carbon emissions.

PRINCIPLES OF FORMING THE OPTIMAL STRUCTURE OF THE ELECTRIC POWER SYSTEM WITH THE INTEGRATED USE OF DISTRIBUTED GENERATION SOURCES

Abstract. The article presents the results of developing the optimal configuration of a hybrid electric power system of distributed generation, taking into account the energy potential of alternative energy in Ukraine. The issues of further development of distributed energy based on the introduction of hybrid systems with renewable sources of distributed energy generation are considered. The potential of alternative energy technologies in Ukraine is analyzed. A comparison of the efficiency of implementation of different types of alternative energy sources by the method of hierarchy analysis is carried out in order to identify their level of prospects for use in complex systems of distributed generation. The general optimal structure of the electric power system of distributed generation is developed. A method for optimizing the composition of integrated distributed generation power systems with renewable energy sources based on expert analysis of energy efficiency and the potential for the development of modern alternative energy technologies in Ukraine is proposed. The application of the performed research will increase the reliability of distributed generation systems and their energy efficiency in the hybrid use of renewable energy sources, provided that the system control is fully automated and its structure is optimised.

Proposal design and thermodynamic analysis of a coal-fired sCO2 power system integrated with thermal energy storage

It is essential to develop supercritical carbon dioxide (sCO2) power systems integrated with thermal energy storage (TES) to achieve efficient and flexible operation of thermal power plants. This study proposes a novel integrated configuration of the sCO2 coal-fired power system and TES. The extracted sCO2 from the high-pressure turbine inlet is utilized as the source of the stored heat, and the extracted heat is used for storage, work, and recovery in steps to decrease the feasible lowest power load ratio. While in the discharging process, the stored energy is used to heat the branched sCO2 to increase the output power. Thermodynamic models for both design and off-design conditions are developed, and the multi-parameter optimization is carried out by genetic algorithm aiming at the highest energy efficiency. The energy conversion characteristics of the heat storage/release process are obtained, and the impacts of variations in the sCO2 extraction ratio and the initial temperature of the molten salt on the performance are analyzed. The results indicate that the adjustable power load range of the integrated system widens with an increase in the sCO2 extraction ratio. When the extraction ratio reaches its maximum value of 0.2749, the minimum power load of the charging process can reduce from 30 % of the rated load to 10.47 %, and the load of the discharging process can increase from 75 % of the rated load to 88.20 %, thus enhancing the operational flexibility. At this point, the round-trip efficiency of the TES system is 67.56 %, which is mainly limited by the performance of the charging process, especially the throttling loss of the turbine. Furthermore, as the temperature of the molten salt cold tank increases, the heat storage capacity decreases, while the minimum power load of the unit increases, thereby reducing the system's flexibility.

Development of a stand-alone photovoltaic (PV) energy system with multi-storage units for sustainable power supply

The sizing of the energy components is essentially designed to prevent outages and ensuring the reliability of the power supply. This paper focuses on the development of a stand-alone photovoltaic/battery/fuel cell power system considering the demand of load, generating power, and effective multi-storage strategy using a probabilistic sizing algorithm. A computer program was developed and used in the design of component sizing configuration of a stand-alone power system that comprises of a photovoltaic generator (PV), battery, water electrolyzer, a storage gas tank, a fuel cell, and an inverter for a reliable power supply. This program manages the energy flow through the various components of a stand-alone PV/battery/fuel cell power system and provide an optimal technical configuration. The optimum system configuration of a residential building with daily power demands of 69 kWh/day energy consumption is composed of PV arrays resulting in total rated power of 15 kW, 16 units of 6 V, 225 Ah battery bank, 5.5 kW fuel cell, 5.5 kW Water Electrolysis, 16.5 kg hydrogen tank, and a 5.5 kW inverter. Based on the simulation results conducted, it was shown that the sizing and development of a stand-alone PV/battery/FC energy system have been achieved with system reliability (loss of power supply equal to zero). This program could be used as a power monitoring and control system for a stand-alone PV/battery/fuel cell power system.

Assessing the impact of power dispatch optimization and energy storage systems in Diesel–electric PSVs: A case study based on real field data

This research paper presents an in-depth analysis of diesel–electric power systems in offshore Platform Supply Vessels (PSVs). The main contribution is the use of real data obtained from a PSV to produce and validate computational models subsequently used to accurately calculate fuel consumption and emissions, with representative load demands, considering different configurations of non-hybrid and hybrid power systems with the addition of lithium-ion batteries to provide a reliable analysis. Power dispatch optimization had a significant impact, achieving fuel and CO2 reductions of around 7%–12% in non-hybrid systems, depending on the generator loading limits. Hybridization and battery integration further enhanced reductions, with up to 10% improvements in fuel consumption and CO2 emissions. Particulate matter (PM) emissions and running hours were substantially reduced, but NOx varied. Validity checks accounted for biases, and the study provided reliable assessments of trends. The differences between monthly averages and robust approaches were minimal. Overall, the strategies presented achieved reductions of up to 15% in fuel consumption and CO2 emissions, 20% in NOx emissions, and 68% in PM emissions for the complete mission.

Flexibility options in a decarbonising iron and steel industry

The decarbonisation of the iron and steel industry is expected to significantly increase its electricity consumption due to higher levels of electrification and the partial shift to hydrogen as iron reductant. With its batch processes, this industry offers large potential for the application of demand response strategies to achieve electricity cost savings. Previous research has primarily focused on investigating the demand response potential for currently operating manufacturing processes and partly for future low-carbon processes. This study aims to consolidate this knowledge and apply it to a modelling analysis that investigates the demand response potential of two new low-carbon technologies: the hydrogen-based direct reduction of iron with electric arc furnace technology (H2-DRI-EAF) and the blast furnace basic oxygen furnace technology retrofitted with carbon capture (BF-BOF-CCUS). A cost optimisation approach is applied to plant configurations with varying parameters relevant for flexibility, such as electrolyser and storage sizes, and in the context of future electricity prices. Multiple price profiles are selected to encompass uncertainties on the development of the power system. The potential for a H2-DRI-EAF plant is 3–27 times higher than for a BF-BOF-CCUS, with electricity costs savings potentials of 35% and 3%, respectively. The study finds that electricity prices have the most significant impact on the profitability of investing in electrolyser overcapacities, which enable operating costs reduction. Therefore, the profitability of these investments are strongly dependent on future power system configurations.

Critical zone identification framework for bulk electric system security assessment

Climate change introduces a new set of vulnerabilities. It might cause unexpected voltage and frequency violation problems that can damage the existing power system configurations. Identifying the zones containing more critical violations in a bulk system under different climatic conditions and contingencies is crucial to securing the power system operation and infrastructure safety. This paper presents an advanced critical zone identification framework that can retrieve information from extensive data yielded by dynamic contingency analysis results from a large interconnected power grid. A machine learning (ML) based clustering approach is applied to identify the critical zone under hundreds of scenarios. The developed framework is tested using the 2028 western electricity coordinating council (WECC) system, and the detailed results are discussed in this paper.

Dynamic wide‐area cooperative protection: A new approach

AbstractThis paper introduces a new dynamic wide‐area cooperative protection based on cooperative control of distributed multi‐agent systems and graph theoretical methods. For power system cooperative protection, a new voting method is designed that manages each agent's cooperation according to each protection area. Cooperative protection areas work together and make a wide area of cooperative protection for the specific protection scheme. As the power system configuration varies, the voting management and the wide‐area cooperative protection dynamically change. The proposed protection method decisions are collective, while a centralized master does not exist, and local information is used. A fault detection code and a wide‐area fault detection code are presented for fault detection. The suggested dynamic wide‐area cooperative protection scheme is tested on an IEEE 39‐bus test system. The proposed protection performance is studied as backup protection for distance relays during normal, stable power swings, unstable power swings, and load encroachment conditions.