Power Generation Network Research Articles

Three network design problems for community energy storage

AbstractIn this article, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid configurations are modeled using a unifying mixed‐integer linear programming formulation. These configurations represent three different business models, namely: the island model, the interconnected model, and the Energy Service Companies model. The proposed mathematical formulations determine the optimal households' aggregation as well as the location and sizing of CES. To overcome the computational challenges of treating operational decisions within a multi‐period decision making framework, we also propose a decomposition approach to accelerate the computational time needed to solve larger instances. We conduct a case study based on real power consumption, power generation, and location network data from Cambridge, MA. Our mathematical models and the underlying algorithmic framework can be used in operational and strategic planning studies on smart grids to incentivize the communitarian distributed renewable energy generation and to improve the self‐consumption and self‐sufficiency of the energy sharing community. The models are also targeted to policymakers of smart cities, utility companies, and Energy Service Companies as the proposed models support decision making on renewable energy related projects investments.

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

Power generation–network–load–energy storage co-planning under uncertainty

With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning.

Design and performance analysis of hybrid optimization MPPT controller for proton exchange membrane fuel cell system with DC-DC converter

At present days, the hybrid Electric Vehicle (EV) utilizing the fuel stack power generation network. Most of the fuel cell output characteristics are nonlinear fashion. Also, its operating point fluctuates based on the input temperature of the fuel stack. So, the receiving of peak voltage from the fuel source is less. To enhance the voltage delivering capability of the fuel stack, an adaptive Cuckoo Search Controller (CSC) based fuzzy logic method is included in the Maximum Power Point Tracking (MPPT) block. The proposed controller is compared with the other basic hybrid controllers which are Perturb & Observe based Artificial Neural Network (P&O with ANN) controller, plus Hill Climb dependent ANN controller (HC with ANN). The major features of proposed power point finding controller are few steady state distortions, few voltage ripples, high tracking convergence speed, plus less iterations required for catching the MPP position. The fuel stack generation voltage is less. In order to increase the voltage of the proposed network, the boost converter is applied. The entire system is studied by the use of MATLAB/Simulink software.

Signal processing on graphs for estimating load current variability in feeders with high integration of distributed generation

The emergence of new elements as distributed generators in power networks is a challenge for supplying energy with quality, reliability and continuity. The impacts of distributed power generation (DG) in these systems vary according to the number of generators, power and topological position of DG and feeder topology. Due to this, power systems simulations are essential to analyze the feeder behavior in this new scenario ensuring a good compliance with electricity quality levels. This paper utilizes a graph-theory based model and proposes a novel method that associates concepts of power flow and graph signals to identify the load current variation in a bus of a distribution feeder with DG, for a steady-state analysis. Graph Theory is used to model the feeders, where the nodes represent the feeder buses. The graphs were performed with Signal Processing on Graphs approach and the graph signal is defined by each bus weight, determined by the power flow assigned to that bus, calculated by algebraic power flow. To fit and validate the methodology, the results obtained are related with the current variation values obtained by solving a power flow problem, using Simulink data. Results were presented for 13 and 34 bus electrical grid systems. The Spearman’s and Pearson’s rank-order correlation showed a good agreement between the results of the graph analysis and the Simulink data. The method proposes an alternative way to identify the topological position of the generator in a feeder that most impact in the current variation, reducing the need of an electrical software and allowing an analysis with large amounts of DG connected to the feeder, which is uncommon in the literature, due to complexity in simulation and computational cost. The methodology can be applied in power utilities studies to analyze the feeders with the DG in the power grid.

Command Value Determination Considering Generation Cost and Control Performance Using Model Predictive Control in Load Frequency Control

This paper deals with load frequency control (LFC) problem for power system. The control is conducted to retain power demand supply balance by controlling output of generators in power network. In order to realize LFC with low power generation cost while maintaining control performance, this paper proposes frequency control method using Moving Horizon Estimation (MHE) and Model Predictive Control (MPC). First, MHE is used to estimate the state of the system. Next, optimization calculation is performed using MPC to determine the command values for each generator. Finally, simulation of load frequency control is carried out to verify the effectiveness of the proposed method.

Cascade feedforward neural network and deep neural network controller on photovoltaic system with cascaded multilevel inverters: Comparison on standalone and grid integrated system

The introduction of a micro-grid-based power generation network will help to meet the demands of consumers while reducing environmental impact. Several industrialized and emerging countries allocate considerable resources to renewable energy-based power generation and invest significant sums of money in this area. This study examines the challenges involved with electricity generation through photovoltaic (PV) systems and the integration of the same with the grid to mitigate power quality issues and improve the power factor for various loading conditions. An innovative multilayer inverter for grid-connected PV systems has been developed to enhance the voltage profile and resulted in a drop in total harmonic distortion (THD). A cascade multilevel inverter (associated with a grid-integrated PV system and managed using multiple innovative artificial intelligence controllers) was developed in this research project. Various advanced intelligent controllers, such as cascade feedforward neural networks (CFFNN) and deep neural networks (DNN), have been analyzed under various operating situations and observed that the THD of voltage, current at the grid, and the load is less than 3 % as per the IEEE 519 standards along with this power factor is maintained nearly unity for the grid-connected system. The quality of power in terms of voltage, frequency, total harmonics distortion, and power factor are improved by using a novel deep neural network algorithm in a cascaded multilevel inverter and verified according to IEEE 1547 and IEEE 519 standards to determine the efficacy of the proposed system.

Discovering Irregularities from Computer Networks by Topological Mapping

Any number that can be uniquely identified and varied by a graph is known as a graph invariant. This paper will talk about three unique variations of bridge networks, sierpinski networks, honeycomb, and hexagonal networks, with great capability of forecast in the field of software engineering, arithmetic, physics, drug store, informatics, and chemistry in setting with physical and chemical properties. Irregularity sombor invariant is newly introduced and has various expectation characteristics for various variations of bridge graphs or other networks, as mentioned. First, find the irregularities in the networks with the help of the Irregularity sombor index. This will be performed in a step by step procedure. The study will take an existing network, associate it with a graph after finding their vertices and edges, then solve the topology of a graph of a network. Graphical results demonstrate the upper and lower bounds and irregularities of certain networks, and mathematical results are used for modeling purposes. The review settled the topologies of graphs/networks of seven distinct sorts with an Irregularity sombor index. These concluded outcomes can be utilized for the demonstration and modeling of computer networks such as local area networks, Metropolitan area networks, Wide area networks, memory interconnection networks, processor interconnection networks, the spine of the internet, and different networks/designs of Personal computers, power generation networks, mobile base station and chemical compound amalgamation and so on.

Optimal Topology Design for Distributed Generation Networks Considering Different Nodal Invulnerability Requirements

Distributed generators and microgrids are of great importance for the stable operation of power systems when failures occur. The major work of this paper is proposing an optimal topological design model of preset connection lines, aiming at a distributed power generation network based on different nodal invulnerability requirements. Moreover, the important innovation of this paper lies in that the perspective is shifted from the system to an individual node of a different type. When a node malfunction occurs, the faulty node can be connected to its neighbor nodes by closing a switch to achieve energy exchange. The distributed generation network consists of a series of nodes that can realize self-sufficiency and can be classified into three types with different levels of importance according to their tasks. The nodes of different types must meet different requirements of destructibility. In this paper, a mixed-integer linear programming model is formulated to solve the optimal topology design problem. In addition, this paper also analyzes the influence of changing nodal power generation capacity and nodal type, and the simulation results show the practicability of the proposal.

An Advanced Boundary Protection Control for the Smart Water Network Using Semisupervised and Deep Learning Approaches

Critical infrastructures across many industries, such as smart water treatment and distribution networks (SWTDNs) and power generation and public transport networks, depend on the supervisory control and data acquisition (SCADA) system. However, being the core component of the critical infrastructures, it has made the SCADA-based SWTDN system an attractive target for cyberattacks. A successful attack on the SCADA will have a devastating impact on an SWTDN in terms of proper operations; therefore, safeguarding the SCADA from cyberattacks is of paramount. With the increasing cyberattacks on SWTDN, both in number and sophistication, the need to detect these attacks early has become a subject of great interest among practitioners and researchers. To this end, we propose a novel strategy, based on a semisupervised approach. Two semisupervised approaches, including unsupervised learning and deep learning-based approaches, have been proposed. The proposed approaches can involve learning dynamic cyberattack patterns from unlabeled data in an SWTDN. We validate the proposed semisupervised approach experimentally using an operational water treatment plant testbed. The proposed approach achieved almost 100% accuracy and substantially outperforms the existing baseline approaches used in this article. The outcome of the experiment is encouraging and demonstrates the potential use of the semisupervised approach for security control in smart water distribution.

Discussion on the Coordinated Development of Distributed Photovoltaic Power Generation and Active Distribution Network

Discussion on the Coordinated Development of Distributed Photovoltaic Power Generation and Active Distribution Network

Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency

Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security-constrained unit commitment (SCUC) model to clear day-ahead energy and reserve markets considering transportation–electricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre-contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high-impact outages. To address this issue, generator outages ranked first are identified using the post-contingency generator response ranking approach. The main contribution of this paper is the development of a structure with the most effective outages in the presence of uncertain wind power and transportation networks. The stochastic optimization approach is modeled through scenarios with corresponding probabilities to manage the wind uncertainty. The computationally complex proposed model is solved by a prominent bender decomposition approach. Case studies are shown on a modified IEEE 118-bus system with 26 nodes and 74 connected links of the electricity transportation network. The results show that optimal EV travel scheduling can optimize the transportation network and a sufficient energy reserve, confirming the security of the power system with minimum operating cost.

Solar Photovoltaic Interfaced Quasi Impedance Source Network Based Static Compensator for Voltage and Frequency Control in the Wind Energy System

In this paper, the purpose of improving the power quality of the standalone wind conversion system (WECS) is to utilize a quasi-impedance (qZ) static compensator (STATCOM) integrated solar photovoltaic (SPV) source network. The standalone WECS has utilized the flywheel energy storage system (FESS) and the self-excited induction generator (SEIG). The SPV power generation network integrates with the STATCOM DC-Link to compensate for the voltage and frequency fluctuations over a longer time. The SEIG terminal voltage and STATCOM DC-link voltage are regulated using an enhanced second-order generalized integrator (ESOGI) with a fuzzy logic controller (FLC). The coordination logic is proposed in order to handle the various scenarios and make the device energy efficient. The unit is built for optimal usage of the electrical energy generated from renewable sources by utilizing physical energy storage systems such as flywheel and battery. In both batteries and flywheels, excess power from the SPV and wind turbines is collected. To verify the performance of the SPV supported STATCOM, the simulation and experimental studies are performed under specific load conditions. This paper describes an optimal way to produce and utilize green power with better energy efficiency. With various current conditions, the typical level of harmonic distortion of the source current is about 1.4% lower than that specified by IEEE Std. 519–2014 for the tolerable 5% level.

Research on interval multi-objective optimal power flow in AC-DC systems considering wind power fluctuation

Aiming at the influence of the uncertainty of power system operating parameters such as wind power fluctuation on AC-DC hybrid system, an interval optimal power flow calculation method based on interval and affine arithmetic is proposed in this paper. First, AC and DC interval power flow model is constructed based on the relationship between interval and affine arithmetic, and the uncertainties such as the new energy generation output of the system are expressed as interval variables; static security performance index (PI) is introduced in AC-DC multi-objective optimal power flow objective functions, which take the system’s power generation cost and network loss into account; the Pareto optimal solution set is distributed uniformly in space by using the particle swarm algorithm to solve the interval optimal power flow model. Finally, MATLAB simulation examples are used to verify that the method can optimize the system’s power generation cost, network loss and static safety index while considering wind power fluctuation.

Research on the interconnection and rapid deployment of smart energy service platforms under multi-factor game balance

This paper firstly models the relationship between power companies, photovoltaic power generation, wind power generation networks, and users as a three-stage Steinberg game. In this game model, the power company is the dominant player and the leader of the entire game. Secondly, with the game model, a detailed analysis is carried out to obtain the optimal strategy of each game party. Finally, the simplified model was simulated and verified. The simulation result is consistent with the theoretical solution result. The research results of this paper have improved the level of energy services and innovated energy service models.

Modelling and control of solar thermal power generation network in smart grid

The thermal storage system is an essential part of the trough solar thermal power generation system. Due to the strong randomness, intermittency, and volatility of solar energy resources, to further improve the system?s overall reliability to meet the needs of variable operating conditions, the paper optimizes the control strategy of the trough solar heat storage system. Taking the molten salt heat storage medium in the oil/salt heat exchanger, the core equipment of the heat storage system, as the critical research object, the article adopts proportional, integral, and differential (PID) control theories. It builds the system in the MATLAB/Simulink simulation environment mathematical model. We use the critical proportionality method to determine many critical parameters in the control system, tune the proportional coefficient, integral time, and other physical quantities in the PID controller, and analyze the proportional control, proportional-integral control, PID controls the respective dynamic response characteristics of these three different control systems. The simulation and comparative analysis results show that: compared with the other two control methods, PID control can effectively weaken the heat storage system oscillation caused by external disturbance, its dynamic response speed is faster, the adjustment time is shorter, and it can meet the requirements of operational stability. The paper adopts PID control, which reduces the control difficulty of the trough solar heat storage system and improves the adaptability to changes in external meteorological resources. The research results have particular guiding significance at the academic and engineering levels.

Synthesis and design of sustainable integrated process, water treatment, and power generation networks

A concept of sustainable synthesis, design, and innovation of integrated chemical processes with the overall objective to achieve zero or negative waste emissions is proposed. Available potential energy sources for power and heat generation processes are integrated into a consolidated energy supply sub-network. Available possible water sources and treatment technologies including wastewater recycling are integrated into a water sub-network. For identified target products, existing and potential processing routes are integrated into a superstructure representing a process sub-network. The above three sub-networks when combined lead to the formation of a consolidated optimization framework, which represents the multiple sub-networks at different scales and includes environmental impacts as constraints, for an integrated sustainable synthesis-design problem definition. A decomposition-based strategy is employed to solve the optimization problem to obtain the design of a totally integrated and sustainable process with positive economic benefits and low energy consumption, together with low carbon and pollutant emissions. The applicability of the framework and solution steps are illustrated through a conceptual but realistic case study under different scenarios.

A stronger energy strategy for a new era of economic development in Vietnam: A quantitative assessment

Energy security has been a major concern in developing countries because of rapid economic development and population growth, low power generation capacity, and unwell-developed transmission infrastructure. Vietnam in this context has been under energy security threats for more than a decade and is currently having a new power policy to strongly develop power generation and distribution networks. It is expected that the country's economy is able to develop substantially due to massively additional energy supply once completing the plants and distribution networks but the likely impacts are still undefined. This paper extends an economic electricity-detailed model to examine the potential impacts of such a new power policy in Vietnam. We find that the policy will decrease the prices of both fossil-based and renewable-based electricity significantly by 40%–78% under a 2030 target scenario, benefiting all sectors in the economy to substitute for fossil fuels. Households are particularly benefited as evidenced by 5.64%–19.19% increases in per-capita utility. Overall, the Vietnamese economy is significantly advantaged with real GDP increasing by 5.44%–24.83% over different scenarios, which are much higher than the findings in other countries. More importantly, the policy moves the country to a low-carbon energy structure in the near future.

Optimal Design of the Power Generation Network in California: Moving towards 100% Renewable Electricity by 2045

Optimal Design of the Power Generation Network in California: Moving towards 100% Renewable Electricity by 2045

On-Site Energy Consumption Technologies and Prosumer Marketing for Distributed Poverty Alleviation Photovoltaic Linked to Agricultural Loads in China

In China, photovoltaic poverty alleviation projects play important role in promoting social and economic development in poverty-stricken areas. They can help stabilizing and increasing incomes of the poor. However, poverty alleviation photovoltaics are mainly connected to the rural distribution network in large numbers, which have a greater impact on the safe and stable operation of the distribution network, making the rural distribution network difficult to solve the problem of collecting and consuming distributed photovoltaic energies on-site. Meanwhile, photovoltaic poverty alleviation projects mainly rely on subsidies from the government and power grids, and the prosumer marketing methods are inadequate. In this paper, an effective on-site consumption technology for photovoltaic power generation linked to agricultural load for poverty alleviation is discussed, together with new energy management and planning technologies for stable two-way energy flow between photovoltaic power generation and rural distribution networks, which can increase the photovoltaic power supply radius, and deal with load fluctuations, and perform precise reactive power control of agricultural load. Furthermore, a prosumer marketing that can simultaneously create maximum economic benefits for power grid and poverty alleviation targets is proposed. The detailed economic benefits are calculated for a demonstration project in Funan county, Anhui province, China.