Existing Power Grid Research Articles

Large-scale wind power grid modelling and stability evaluation using stochastic approaches

With the global demand to increase the level of low-carbon renewable energy resources (RERs) for electric power generation, dwindling fossil fuel reserves, and concerns that fossil fuel emissions are leading to climate change with possibly disastrous consequences, research is underway to discover the probable large-scale usage of RERs that will not be integrated into an existing power grid. A large-scale RER power grid is anticipated to run independently and be stable and dependable, like a traditional system. Wind power generation is no longer negligible. Wind energy is one of the most established renewable power options for ensuring RE self-sufficiency, but it's also one of the ficklest (RERs). Wind power generation is predicted to rise quickly in the next years owing to interest in renewable energy to counteract global warming. National or regional RE power networks are the subjects of growing study. The issue is the grid's stability and dependability. Wind farms' production variability, intermittency, and load mismatch can damage grid voltage stability. In answer to this difficulty, a large-scale wind power system was modeled using a stochastic methodology, and the results were analyzed using the Lyapunov technique, matrix exponential, and Hurwitz criteria to determine the future stability of a 100% RE grid.

Sizing Renewable Generation and Energy Storage in Stand-Alone Microgrids Considering Distributionally Robust Shortfall Risk

Renewable generation has grown rapidly these years due to its advantages in low environmental impacts. Stand-alone microgrid with renewable generation and energy storage is a promising option in remote areas beyond the reach of an existing power grid. This paper proposes a multiobjective optimization model to co-optimize the sizes of renewable generation and energy storage in stand-alone microgrids, which minimizes the load shedding risk and the total investment cost. The load shedding degree is obtained by an optimal operation problem and measured by shortfall risk, considering the uncertainties of demand and renewable resources. To deal with the inaccurate empirical distribution, distributionally robust shortfall risk is further established under a Wasserstein-metric-based ambiguity set, and an equivalent distributionally robust formulation is obtained. Then a conservative approximation is developed, and the resulting problem appears to be a bi-objective linear program. The Pareto frontier is proven to be piecewise linear, and the analytical expression of the Pareto frontier is derived. Case studies demonstrate the effectiveness and performance of the proposed method.

An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition.

Smart grid is regarded as an evolutionary regime of existing power grids. It integrates artificial intelligence and communication technologies to fundamentally improve the efficiency and reliability of power systems. One serious challenge for the smart grid is its vulnerability to cyber threats. In the event of a cyber attack, grid data may be missing; subsequently, load forecast and power planning that rely on these data cannot be processed by generation centers. To address this issue, this paper proposes a transfer learning-based framework for smart grid scheduling that is less reliant on local data while capable of delivering schedules with low operating cost. Specifically, the proposed framework contains (1) a power forecasting model based on transfer learning which can provide high quality load prediction with limited training data, (2) a novel adaptive time series prediction method with modeling time series from a covariate shift perspective that aims to train the forecasting model with a strong generalization capability, and (3) a day-ahead optimal economic power scheduling model considering a shared energy storage station.

In-motion charged vehicle simulation considering traffic and power grid interactions

When designing new electric vehicles for urban transport, both vehicle producers and operators need to establish and verify requirements that the vehicle has to fulfil regarding, e.g., energy storage capacity, driving range, or battery wear. These requirements are typically verified using simulation tools that concentrate mostly on electrical quantities, but disregard the influence of road infrastructure, surrounding traffic and detours. At the same time, an expansion of the electric vehicle fleet may have a negative impact on the existing power grid. Sufficient dimensioning of the grid elements and their verification are therefore necessary to keep the electric transport service reliable. This paper demonstrates a tool suitable for verification of battery-assisted trolleybus fleet and power infrastructure parameters, based on extensions to the SUMO traffic simulator. The tool carries out a joint simulation of electric and traffic-related quantities. It is demonstrated on seven use-cases inspired by real-life problems. • A tool to verify battery-assisted trolleybus fleet configuration is presented. • Includes fast DC power grid solver for electric grid analysis. • Combines traffic and electric circuit simulation to study mutual interactions. • The effect of surrounding traffic on vehicle power demands is considered. • Study of different parameters of a sample trolleybus network is presented.

Efficient energy management of wireless charging roads with energy storage for coupled transportation–power systems

Wireless charging roads equipped with energy storage systems are promising electric vehicle charging solutions by virtue of their strong advantages in time saving and reduced pressure on the existing power infrastructure. Integration of wireless charging roads into the existing electricity market and efficient management of the corresponding energy storage system are crucial for successful implementation of the wireless charging road systems. In this work, we develop a coupled transportation–power system framework for incorporation of a wireless charging road system into the real-time electricity market. In addition, we propose a Lyapunov optimization-based control strategy to manage the energy storage system in a cost-efficient manner. Our simulation study demonstrates that efficient control of the energy storage system not only reduces the energy costs of the entire wireless charging road system but also alleviates the pressure produced by the wireless charging load on the existing power grid. In our two numerical examples, the energy costs are reduced by 2.61% and 15.34%, respectively. Two indicators of power grid pressure: time average of maximum and time average of standard deviation of locational marginal prices, are reduced by 10.65% and 69.33% for the first numerical example and 5.11% and 34.73% for the second numerical example.

Photovoltaic Power Forecasting based on Artificial Neural Network and Ultraviolet Index

The accuracy of photovoltaic (PV) power generation forecast can seriously affect the penetration ability of PV power into the existing power grid, which is one of the key approaches to achieve emission peak, as well as realize carbon neutrality. In the conventional forecasting methods, Global Horizontal Irradiation (GHI), Diffuse Horizontal Irradiance (DHI), temperature, wind speed, rainfall, etc. are considered as the mainly factors to forecast the PV output power, but ignore the impact of PV power generation caused by the whole PV system’s decay over the 25–30 years lifecycle. The ultraviolet (UV) index, which reflects the quantity of 10–400 nm irradiation, has a strong correlation with such decay and power generation. This paper proposes a novel PV power forecasting model that involving UV index in an artificial neural network, using Adam method to optimize the training process with the Keras-tuner employed for optimization of the hyperparameters. Experiments demonstrate that the proposed model achieves more precise performance than conventional methods.

A Novel Solution for Solving the Frequency Regulation Problem of Renewable Interlinked Power System Using Fusion of AI

The requirement for clean energy has increased drastically over the years due to the emission of CO2 and the degrading of the environment by introducing Renewable Energy Systems (RES) into the existing power grid. While these systems are a positive change, they come at a cost, with some issues relating to the stability of the grid and feasibility. Hence, this research paper closely investigates the modeling and interlinking of photovoltaic (PV)-based solar power and Double-Fed Induction Generator (DFIG)-based wind turbines with the conventional power systems. RES has been known to contribute to a highly non-linear system and complexity. To return the power systems to their original state after a load disturbance, a novel control technique based on the fractional-order Type-2 Fuzzy logic system, well developed via particle swarm optimization (PSO), has been utilized for solving the frequency control problem of a renewable interlinked power system. The efficacy of the proposed technique is validated for various possible operating conditions and the system results are compared with some of the recent methods with and without including non-linearity, and the performance of the controllers is superimposed on frequency/time graphs for ease of understanding to show the benefits of the proposed research work.

Application of Computational Fluid Dynamics and Process Modeling to Investigate Low-Load Operation of a Subcritical Utility-Scale Boiler

Abstract To enable the inclusion of intermittent renewable energy sources on existing power grids requires base-load coal-fired power plants to operate flexibly, including fast load changes and continuous low-load operation. Low-load operation of a 620 MWe subcritical boiler is analyzed with the aid of a cosimulation methodology that incorporates a detailed 3D computational fluid dynamics (CFD) model together with a one-dimensional process model. A discretized one-dimensional model of the water/steam circuit was developed for the furnace, the radiant superheaters, and the convective/back pass heat exchangers. This is coupled with a detailed CFD model incorporating the furnace and radiant superheaters using an Eulerian–Eulerian reference frame. The coupled model is used to investigate the combustion stability, water/steam side effects, and the radiant heat-exchanger operational limits for six different burner firing arrangements at a low boiler load of 32% of maximum continuous rating. The results show that certain firing arrangements can lead to a high risk of fire-side corrosion and overheating of heat-exchanger components. Based on the analyses of combustion stability, boiler efficiency, and the safe operation of heat-exchanger components, a mixed firing arrangement with a higher secondary air mass flowrate for nonfiring burners was selected as the best operational strategy at this low load for the boiler under investigation.

Location and Technical Requirements for Photovoltaic Power Stations in Poland

The objective of Poland’s energy policy is to guarantee energy security while enhancing economic competitiveness and energy efficiency, thus minimizing the power sector’s environmental impact and optimizing the use of energy resources in the country. Poland is not the only European country to rely on coal for power generation. Historical factors and large coal deposits act as natural barriers to increasing the share of renewable energy in the Polish power sector. Yet, today, environmental concerns and climate change are prompting many countries to move away from fossil fuels. Renewable energy sources, such as solar and wind energy, are an alternative to traditional energy generated from fossil fuels. However, investors developing solar and wind farms in Poland encounter numerous problems at each stage of the project. These difficulties are associated mainly with the location, technical requirements, infrastructure and formal and legal documents. This study aimed to identify the key factors that influence the development of photovoltaic power stations in Poland, with special emphasis on the choice of location and technical aspects of the investment process. The demand for clean energy and the renewable energy prospects for Poland are discussed based on the example of solar farms. Sixty-seven prospective farm locations were analyzed, and the results of the analysis were used to identify the main barriers and opportunities for renewable energy development in Poland. The option of connecting solar farms to the existing power grid was also examined. This study demonstrates that the development of solar farms in Poland is inhibited mainly by technical barriers, in particular the lack of options for connecting farms to the power grid, as well as the absence of support mechanisms and dedicated legislative solutions, rather than environmental obstacles.

Recent Advances on Distributed Dispatching and Control Algorithms in Virtual Power Plant

In recent years, the concept of Virtual Power Plant (VPP) is becoming more and more popular due to its capability of aggregating Distributed Energy Resources (DERs) and exploring its potential of providing flexibility to the existing power grid. Distributed algorithms have several potential advantages over centralized approaches, such as sharing limited amounts of information, performing parallel computations, and having strong robustness. In this context, this paper surveys the literature of distributed algorithms regarding VPP applications. Firstly, we established the VPP framework and operation control modes. Secondly, the basic distributed algorithms were given. Then, this paper studied distributed algorithms in VPP for economic dispatch, bidding optimization, autonomous control strategy within VPP, coordinated control strategy among muti-VPP, and interactive control strategy with grid side. Finally, the key issues and research potential were analyzed.

Appraising the Optimal Power Flow and Generation Capacity in Existing Power Grid Topology with Increase in Energy Demand

Several socioeconomic factors such as industrialization, population growth, evolution of modern technologies, urbanization and other social activities do heavily influence the increase in energy demand. A thorough understanding of the effects of energy demand to power grid is highly essential for effective planning and operation of a power system network in terms of the available generation and transmission line capacities. This paper presents an optimal power flow (OPF) with the aim to determine the exact nodes through which the network capacities can be increased. The problem is formulated as a Direct Current (DC) OPF model, which is a linearized version of an Alternating Current (AC) OPF model. The DC-OPF model was solved as a single period OPF problem. The model was tested in several case studies using the topology of the IEEE test systems, and the computation speeds of the different cases were compared. The results suggested dual variables of the problem’s constraints as an extra tool for the network designer to see where to increase the network capacities.

Energy Storage Applications to Address the Challenges of Solar PV and Wind Penetration in Indonesia: A Preliminary Study

Indonesia intends to increase the renewable energy ratio to at least 23% from the energy mix generated by 2025. This target is also in line with the Paris Agreement that Indonesia ratified in October 2016. However, renewable energy capacity has not been significant, as 11.38% of the total on-grid power capacity (MEMR, 2021). More than 90% of renewable comes from hydropower and geothermal, and only a limited capacity comes from wind and solar energy. On the other hand, wind and solar energy potential are enormous for energy generation in Indonesia. One of the barriers that hinder the use of both is their intermittent nature so that they are not economically profitable and can disrupt the existing power grid. Energy storage systems (ESS) can reduce this intermittent problem as frequency regulators and voltage support to the grid. This paper reviews the potential and challenges of energy storage and renewable power generation, especially wind and solar power. This paper also outlines lessons learned from energy storage systems that have been implemented and are still under development. The discussion focuses on the types of energy storage suitable for applications in Indonesia.

Research on intelligent peak-cutting and valley-filling charging and swapping mode based on potential game theory

With the continuous increase in the proportion of renewable energy represented by wind and light in the power grid in recent years, on the one hand, the energy structure of the existing power grid has been directly optimized, and on the other hand, the full utilization of renewable clean energy can also reduce traditional power generation. The pollution caused by the industry to the environment. As an indispensable infrastructure for electric vehicles, charging and swapping stations, after being connected to a distributed micro-grid, can play a role in reducing peaks and valleys, promoting the consumption of new energy, and saving costs through reasonable configuration. The analysis of calculation examples shows that the intelligent charging and swapping system model based on the potential game theory proposed in this paper can effectively reduce the operating cost and user cost of the swap station, and effectively improve the level of wind and solar consumption, and achieve the level of smoothing the grid curve and peak-shaving and valley-filling. Purpose has important practical significance and engineering value.

Combining offshore wind and solar photovoltaic energy to stabilize energy supply under climate change scenarios: A case study on the western Iberian Peninsula

The expansion of marine renewable power is a major alternative for the reduction of greenhouse gases emissions. In Europe, however, the high penetration of offshore wind brings intermittency and power variability into the existing power grid. Offshore solar photovoltaic power is another technological alternative under consideration in the plans for decarbonization. However, future variations in wind, air temperature or solar radiation due to climate change will have a great impact on both renewable energy resources. In this context, this study focusses on the offshore energy assessment off the coast of Western Iberia, a European region encompassing Portugal and the Northwestern part of Spain. Making use of a vast source of data from 35 simulations of a research project called CORDEX, this study investigates the complementarity of offshore wind and solar energy sources with the aim of improving the energy supply stability of this region up to 2040. Although the offshore wind energy resource has proven to be higher than solar photovoltaic resource at annual scale, both renewable resources showed significant spatiotemporal energy variability throughout the western Iberian Peninsula. When both renewable resources are combined, the stability of the energy resource increased considerably throughout the year. The proposed wind and solar combination scheme is assessed by a performance classification method called Delphi, considering stability, resource, risk, and economic factors. The total index classification increases when resource stability is improved by considering hybrid offshore wind-photovoltaic solar energy production, especially along the nearshore waters.

Multi-Objective Optimal Power Sharing Model for Futuristic SAARC Super Smart Grids

One-fourth population of the world lives in South Asia. The electricity generation for this population consumes the substantial fossil fuels, resulting in the significant emissions of harmful gases in the global environment. To protect the natural environment, South Asian Association for Regional Cooperation (SAARC) has planned a super smart grid for the power sharing between South Asian countries through the integration of the renewable energy resources (RERs). However, the capability of these countries to construct such a mega power grid is unclear due to the unavailability of proper strategic analysis, lack of information about RERs potential, and the lack of decision making models. This paper analyzes the existing power grids in SAARC countries, identifies their electricity generation potential, and develops an integer linear programming model based on the power generation cost, CO₂ emissions, and disruption risk. This study solves the proposed model using goal programming methods. The results exhibited that the SAARC countries have the capability to share the sustainable super smart grids through the substantial integration of RERs. For instance, the model solution based on 100% renewable power (i.e. Case 5) exhibited 36% cost savings, 86% CO₂ emission reduction, and 39% risk reduction compared with corresponding non-renewable power based model solution (i.e. Case 1). In addition, this study performs the cost-benefit analysis for the existing and proposed power systems of SAARC countries. The results provide the various insights to policy makers and practitioners for the strategic decision making related to the SAARC super smart grid. In addition, the methodology of this paper can be adopted for other super smart grids around the world.

Fuzzy Clustering Analysis of Power Incomplete Data based on Improved IVAEGAN Model

The scale of data generated by the complex and huge power system during operation is also very large. With the data acquisition of various information systems, it is easy to form the situation of incomplete power data information, which cannot guarantee the efficiency and quality of work, and reduce the security and reliability of the entire power grid. When incomplete data and incomplete data sets are caused by data storage failure or data acquisition errors, fuzzy clustering of data will face great difficulties. The fuzzy clustering of incomplete data of the power equipment is divided into the processing of incomplete data and the clustering analysis of "recovered" complete data. This paper proposes an IVAEGAN-IFCM interval fuzzy clustering algorithm, which uses interval data sets to fill in the incomplete data, and then completes the clustering of interval data. At the same time, the whole numerical data set is transformed into a complete interval data set. The final clustering result is obtained by interval fuzzy mean clustering analysis of the whole interval data set. Finally, the algorithm proposed in this paper and other machine learning training data sets is made for experimental analysis. The experimental results show that the algorithm proposed in this paper can complete incomplete data sets with high precision clustering. Compared with other contrast methods, it shows higher clustering accuracy. Compared with the numerical clustering algorithm, the clustering accuracy is improved by more than 4.3%, and it has better robustness. It also shows better generalization on the artificial data sets and other complex data sets. It is helpful to improve the technical level of the existing power grid and has important theoretical research value and engineering practice significance.

Optimal Energy Management for Multi-Microgrid Under a Transactive Energy Framework With Distributionally Robust Optimization

The increasing penetrations of renewable energy and electric vehicles bring more uncertainties and challenges to the existing power grid. The coordinated networked microgrids (MGs) contain renewable distributed generations (DGs) and nonrenewable DGs, which will be an important component in the future. We formulate an optimization problem based on a transactive energy (TE) framework for the energy schedule of upstream network and networked MGs to minimize the operation cost. The energy management between MGs and upstream network is operated by the distribution system operator (DSO), which is different from the direct control signal and fixed pricing mechanism in the traditional power system. We develop a distributionally robust optimization algorithm with ambiguity set based on Wasserstein distance (DROW) to solve the optimization problem with the uncertainties from real-time electricity price, renewable energy, loads, and electric vehicles. We carry out case studies about the energy schedule of the modified IEEE 33-bus and IEEE 118-bus power system with networked MGs. Numerical results indicate that the TE framework is conducive to schedule the energy of upstream network and networked MGs efficiently with the dynamic pricing scheme and the proposed DROW algorithm can seek a robust energy schedule of DSO and networked MGs with uncertainties.

Techno-economic optimization of novel stand-alone renewables-based electric vehicle charging stations in Qatar

One of the main challenges of e-mobility roll-out is securing the required charging demand without stressing the existing power grid. The electrical source must be non-conventional to achieve the ultimate eco-friendly goal. This study conducts a techno-economic assessment for a novel stand-alone renewables-based charging station to determine the optimal configuration to generate the required daily charging demand. The optimization is accomplished through modeling and simulating of the proposed design using HOMER software in four cities of Qatar to investigate various geographical locations and metrological conditions. The optimal solution is compared with the grid extension option in each location using a comprehensive economic criterion. The results showed that 250 kW wind turbine with 60 m hub height, 450 kWp CPV/T system, 500 kW electrolyzer, 100 kW H2 and NH3 FCs, 15 kW bio-generator, 200 kg chemical storage tank, 304–324 kW Li-ion battery storage and 299–335 kW converter combination is the optimal stand-alone configuration for the selected sites. The optimum cases' net present cost ranges between $2.53 M to $2.92 M, and the cost of electricity ranges between $0.285 to $0.329 per kWh. The proposed optimization methodology is suitable for applications in any location, considering the metrological conditions of the site under study.

A Dispatch Optimization Model for Hybrid Renewable and Battery Systems Incorporating a Battery Degradation Model

Abstract A recent increase in the integration of renewable energy systems in existing power grids along with a lack of integrated dispatch models has led to waste in power produced. This paper presents a mixed-integer nonlinear optimization model for hybrid renewable-generator-plus-battery systems, with the objective of maximizing long-term profit. Prior studies have revealed that both high and low state of charge (SOC) of the battery is detrimental to its lifetime and results in reduced battery capacity over time. In addition, increased number of cycles of charge and discharge also causes capacity reduction. This paper models these two factors with a constraint relating capacity loss to the SOC and number of cycles completed by the battery. Loss in capacity is penalized in the objective function of the optimization model, thereby disincentivizing high and low SOCs and frequent cycling. A rolling time horizon optimization approach is used to overcome the computational difficulties of achieving global optimality within a long-term time horizon. By incorporating battery degradation, the model is capable of maximizing the profits from the power dispatch to the grid while also maximizing the life of the battery. This paper exercises the model within a case study using a sample photovoltaic system generation time series that considers multiple battery capacities. The results indicate that the optimal battery lifetime is extended in comparison to conventional models that ignore battery degradation in dispatch decisions. Finally, we analyze the relationship between battery operational decisions and the resultant capacity fade.

Elementary changes in topology and power transmission capacity can induce failures in power grids

In this work, we show that elementary changes in the topology of power grids, like the addition or removal of a single transmission line or the increase of its maximum transmission capacity can cause failures in the network. Also, we show that the probability of the occurrence of these failures can be related to the level of centralization of energy generation and to the nature of the nodes being connected by the transmission line being considered, although the increase in the transmission capacity does not seem to be much affected by the level of centralization. When considering a centralized power grid, that is, one grid whose power is supplied by just a few generators, one must be very careful when contemplating a change between two consumers, being an addition, removal or increase in the transmission capacity of the transmission line connecting them, as there is a considerable probability that this change may cause a failure in the network. In the decentralized power grid, the modification that cause most of the failures in the grid is between a consumer and a generator when the removal or increase in the transmission capacity is being considered. Therefore, one must be very careful when planning an update in an existing power grid or when building a new one as a single modification in the grid may lead the system out of the synchronous state.