Grid Resources Research Articles

Development of df/dt function in inverters for synthetic inertia

The increasing share of renewable energy-based resources in the power grid has a detrimental impact on the power system stability. As these resources are inverter-based, they decrease the overall inertia of the grid. To mitigate this problem, recent research focuses on developing novel techniques to provide synthetic inertia with inverters. In this fashion, inverter-based resources (IBR) can also contribute to power system inertia, hence, its stability. Some of the approaches for providing synthetic inertia are discussed in the standards. However, despite its potential, frequency-derivate-based (df/dt) solutions are not studied to adapt grid code in Japan. This paper addresses this need by developing a full df/dt control approach for IBRs. Several intricacies of this design is presented along with operation curves and system designs. Considering the special needs of the Japanese Electrical Grid, and the speed in which inertia support should be provided, a novel rate of change of frequency (RoCoF) measurement approach is also developed and presented. Lab tests are performed with hardware-in-the-loop tests and the results validate the operation and performance of both novel RoCoF measurement and df/dt control model.

Fuzzy Neutrosophic Soft Set Based Transfer-Q-Learning Scheme for Load Balancing in Uncertain Grid Computing Environments

Abstract Effective load balancing is tougher in grid computing compared to other conventional distributed computing platforms due to its heterogeneity, autonomy, scalability, and adaptability characteristics, resource selection and distribution mechanisms, and data separation. Hence, it is necessary to identify and handle the uncertainty of the tasks and grid resources before making load balancing decisions. Using two potential forms of Hidden Markov Models (HMM), i.e., Profile Hidden Markov Model (PF_HMM) and Pair Hidden Markov Model (PR_HMM), the uncertainties in the task and system parameters are identified. Load balancing is then carried out using our novel Fuzzy Neutrosophic Soft Set theory (FNSS) based transfer Q-learning with pre-trained knowledge. The transfer Q-learning enabled with FNSS solves large scale load balancing problems efficiently as the models are already trained and do not need pre-training. Our expected value analysis and simulation results confirm that the proposed scheme is 90 percent better than three of the recent load balancing schemes.

Optimal pump scheduling in multi-phase distribution networks using Benders decomposition

With the growing adoption of renewable energy resources in the power grid, co-optimizing the operation of water and power distribution networks (WDNs and PDNs) increases the flexibility of both systems. The optimal water power flow problem (OWPF) is formulated to manage resource utilization across the WDNs and multi-phase PDNs. The nonlinear WDN hydraulic constraints and PDN ac power flow equations render the OWPF problem nonconvex. This paper formulates a convex optimal water power flow (C-OWPF) problem adopting successive approximations in WDNs and branch-flow relaxations in PDNs. The C-OWPF problem is a mixed-integer semidefinite program, which is computationally challenging even for small instances. Additionally, privacy considerations of WDN and PDN operators motivate the need for solving the C-OWPF in a distributed fashion. This paper develops a C-OWPF solver based on Benders decomposition that overcomes computational complexity challenges and preserve the privacy of the respective operators. The merits of the Benders decomposition-based solver are demonstrated on the IEEE 4-bus PDN coupled with a 3-node WDN and the IEEE 123-bus PDN coupled with the 36-node WDN.

Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids

Given the vitality of the renewable-energy grid market, the optimal allocation of clean energy is crucial. An optimal dispatching method for source–load coordination of renewable-energy grid is proposed. An improved K-means clustering algorithm is used to preprocess the source data and historical load data. A support vector machine is used to predict the cluster of renewable-energy grid resources and load data, and typical scenarios are selected from the prediction results. Taking typical scenarios as a representative, the probability distribution of wind power output is accurately obtained. An optimization model of the total operation cost of the renewable-energy grid is established. The experimental results show that the algorithm reduces the error between the predicted value and the actual value. Our method can improve the real-time prediction accuracy of the renewable-energy grid system and increase the economic benefits of the renewable energy grid.

Rolling-horizon optimization integrated with recurrent neural network-driven forecasting for residential battery energy storage operations

In recent years, the installation of battery energy storage (BES) integrated with solar photovoltaic (PV) panels in residential houses has been rapidly accelerated tied to the high penetration of renewable energy resources, and it is expected to play a significant role as distributed energy resources (DER) in smart grids and microgrid operations. To directly offset electricity demand by efficiently utilizing renewable energy, it is crucial to optimize BES operations in conjunction with forecasting uncertain renewable energy, electricity demand, and prices. Therefore, there is an urgent need to develop proper methodologies to optimize BES operations in residential settings to realize efficient and sustainable smart grid operations under the high penetration of renewable energy. Given this context, this study intends to develop a rolling-horizon optimization model integrated with a recurrent neural network-driven forecasting that is uniquely designed to interactively forecast uncertainty and optimize BES operations in an iterative fashion. Comprehensive numerical experiments have been conducted based on data collected from residential houses to evaluate the performance of the proposed optimization model. The results demonstrate that the proposed optimization model can be practically applied for optimizing residential BES operations while efficiently utilizing solar power in response to time-varying and uncertain electricity demand and prices. Moreover, because the length of the look-ahead period used for both forecasting and optimization should be appropriately set for the proposed model, experiments have been designed and conducted to investigate the impact of the length of a look-ahead period on overall performance.

Economic assessment of multi-operator virtual power plants in electricity market: A game theory-based approach

In recent years, the penetration of distributed energy resources (DERs) has increased significantly due to their tremendous effect on network flexibility, economic indicators, and power loss. On the contrary, a diverse assortment of DERs can lead to some challenges in controlling these resources in the power grid. To acquire the maximum benefit of DERs and overcome their challenges the concept of virtual power plants (VPPs) has been suggested. Due to the ability of VPPs to participate in electricity markets and the competition of VPPs to gain more profit we are facing deregulated multi-operator markets, and it is necessary to define VPPs as price maker units. The optimal economic assessment of VPPs in a multi-operator market depends on two folds: modeling inner cooperation between its components and managing external competition with other VPPs. To this end, in this paper, a new framework for optimal economic assessment of a multi-operator VPP system is proposed by considering a combination of non-cooperative and cooperative game theory-based approaches. In the proposed methodology, VPPs compete with other rivals to determine the amount of power exchange and offer prices based on supply function equilibrium. Due to incomplete information of VPPs about other opponents and market construction, a combination of particle swarm optimization and genetic algorithm is proposed to find the Nash equilibrium point. Also, the Shapely value concept is used for fair distribution of shared profit among VPPs components. The effectiveness of the proposed method has been verified in two case studies for a multi-operator VPP with a diverse assortment of DERs. The results show that VPP profit and electricity market prices directly relate to the diversity of resources in VPP. In this regard, the mark-up coefficient of the VPP with a greater number of DERs is about 16% and 32% larger than the two other VPPs which leads to more profit for this VPP and resources in its coalition.

Optimization Simulation of Balanced Distribution of Multimedia Network Modular Teaching Resources

The balanced distribution of multimedia wireless network modular teaching resources is improved by optimizing and adjusting the balanced distribution of multimedia wireless network modular teaching resources. Based on block grid balanced scheduling and resource spectrum fusion, a balanced distribution method of multimedia wireless network modular teaching resources is proposed. A blockchain Internet of Things accesses control network architecture system for the balanced allocation of multimedia wireless network modular teaching resources established using the priority business scheduling method. Modular resource allocation of multimedia wireless networks and adaptive forwarding are adjusted to control the data packets. The bandwidth guarantee mechanism is used for the equitable allocation of multimedia wireless network modular teaching resources information flow. Multi-service hierarchical management of multimedia wireless network modular teaching resources spectrum is controlled by block grid balanced scheduling and resource spectrum fusion. The high-energy physical computing system structure is used to allocate the resource spectrum balance of the modular teaching resources of the multimedia wireless networks, ensuring the bandwidth requirements of various services. The classified management and balanced allocation of the multimedia wireless networks’ teaching storage, system, and teaching spectrum resources are made possible. The results of the tests show that this method effectively reduces the delay in the allocation of modular multimedia wireless network teaching resources, improves the utilization rate of modular multimedia wireless network teaching resources, and significantly improves their reading and writing performance.

Arduino Interfacing of FLC-based Power Flow Management in Smart Microgrid Systems

There is a rapid increase in power demand which has led to an increase in the need for smaller grids all over the world. The microgrid is such a small power supply system that consists of loads, distributed energy resources, and energy storage systems. With the development of the internet, the power sector has started the development of the newest type of grids, involving a smart technology called smart grids. Smart grids have got smart integration and advanced storage, along with dynamic optimization of grid operation and resources, digital information and control, distributed resources, demand-side resources, energy efficiency resources, and smart metering systems. The developments in intelligent controllers reflect in power flow management also. This paper gives an idea of how to achieve power flow management through a tariff-based Fuzzy Logic Controller (FLC). The control algorithm used here is the k factor-based Icosϕ algorithm, which decides the amount of power shared by microgrids and the grid based on the load demand. The power forecasting of the microgrid with a solar source is performed using ANN. The regression plot and the error histogram obtained show the accuracy of the ANN controller. The simulation of power flow management of the smart microgrid system is tested and analyzed and the proposed system is compared with other existing research. The hardware implementation of the FLC controller with the Arduino interface which is analogous to real-time measurement is explained in detail to compare the simulation results obtained.

On specification-based cyber-attack detection in smart grids

The transformation of power grids into intelligent cyber-physical systems brings numerous benefits, but also significantly increases the surface for cyber-attacks, demanding appropriate countermeasures. However, the development, validation, and testing of data-driven countermeasures against cyber-attacks, such as machine learning-based detection approaches, lack important data from real-world cyber incidents. Unlike attack data from real-world cyber incidents, infrastructure knowledge and standards are accessible through expert and domain knowledge. Our proposed approach uses domain knowledge to define the behavior of a smart grid under non-attack conditions and detect attack patterns and anomalies. Using a graph-based specification formalism, we combine cross-domain knowledge that enables the generation of whitelisting rules not only for statically defined protocol fields but also for communication flows and technical operation boundaries. Finally, we evaluate our specification-based intrusion detection system against various attack scenarios and assess detection quality and performance. In particular, we investigate a data manipulation attack in a future-orientated use case of an IEC 60870-based SCADA system that controls distributed energy resources in the distribution grid. Our approach can detect severe data manipulation attacks with high accuracy in a timely and reliable manner.

Predicting Linearised Wind Resource Grids using Neural Networks

Modelling the flow over terrain is a key element of wind resource assessments within the wind energy industry. Existing flow modelling methods range from fast, low fidelity analytical models to time-consuming and computationally expensive high-fidelity Computational Fluid Dynamics (CFD) software. In this work, a Grid-Kernel Neural Network approach has been developed and used to create surrogate models to emulate the WAsP wind resource software, by calculating the changes in wind speed and direction due to the orography and roughness of terrain. This data-driven approach has proven to be successful in predicting the orographic speed and direction changes at multiple heights above ground. At 100 m above ground, the mean absolute error values were 1.6% speedup and 0.4° for the orographic speed and direction changes, respectively. Although the WAsP model is a linear, potential flow solver, the findings here can be counted as a first step towards creating a fully data-driven CFD wind resource model.

Dynamics of Inverter-Based Resources in Weak Distribution Grids

This work presents the modelling fundamentals to study the dynamics of inverter-based resources (IBRs) in weak distribution grids and derive disturbance performance characteristics describing how they should behave under different conditions. More specifically, with respect to small-signal disturbances we study the possible voltage violations following frequency response from IBRs due to low system strength and also propose specific design requirements for IBR <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d–q</i> current control to guarantee a stable response. In the context of large-signal disturbances, we highlight how active power-voltage control may not be effective due to a delay imposed by the physical features of the distribution network. Further, we mathematically discuss how IBR reactive power control could enhance its phase-locked loop stability. The proposed disturbance performance characteristics are then integrated into the IBR converter control via a novel voltage-priority reference generation strategy. Simulation results on a real Australian network show the efficacy of the proposed operational and control design requirements, and highlight possible unexpected active/reactive power interactions in weak distribution grids.

Sizing of Energy Storage for Grid Inertial Support in Presence of Renewable Energy

Penetration of renewable energy resources (RERs) in the power grid continues to increase as we strive toward a greener environment for the future. While they have many advantages, most RERs possess little or no rotational kinetic energy, thereby threatening the frequency stability of future power grids. Energy storage systems (ESSs) can be used to mitigate this problem, as they are capable of providing virtual inertia to the system. This paper proposes a novel analytical approach for sizing ESSs to provide inertial support to the grid and maintain frequency stability in presence of RERs. This method analytically estimates the total inertia of the system using probability distributions of the outage states of conventional generators and RERs. RERs are modeled as multi-state units, considering the variability of their output power due to changes in weather conditions and also the forced outages of their equipment. The ESS is utilized to provide virtual inertia to the grid in the form of active power once the estimated system inertia falls below the minimum required during a disturbance. The efficacy of the proposed approach is tested on the IEEE 39 bus system and the results obtained by the analytical approach are validated using Monte Carlo simulation.

A co-design framework for wind energy integrated with storage

<h2>Summary</h2> The global growth of wind energy markets offers opportunities to reduce greenhouse gas emissions. However, wind variability and intermittency (across multiple timescales) indicate that these energy resources must be carefully integrated into the power system to avoid mismatches with grid demand and associated grid reliability issues. At the same time, community concerns regarding the local installation of renewable energy and energy storage systems have already delayed or even halted the proposed projects. We propose a broadly defined, co-design approach that considers wind energy from a full social, technical, economic, and political viewpoint. Such a co-design can address the coupled inter-related challenges of cost, technology readiness, system integration, and societal considerations of acceptance, adoption, and equity. Such a successful design depends on the understanding of the needs of relevant communities, the regional grid infrastructure and its demand variability, local and global grid decarbonization targets, available land and resources for system siting, policy and political constraints for energy development, and the projected regional and global impact of these systems on the environment, jobs, and communities.

Pathways to Secure and Stable Island Power Systems [Guest Editorial

In a fast-changing world, island power systems face urgent environmental, economic, sustainability, and social challenges. Historically, island power systems were built to rely on oil-fired generation, causing carbon-intensive power generation, high costs to consumers, and dependance on market price volatilities, thus hindering their economic development and social stability. Reducing or eliminating the dependence on costly imported fossil-fuels has many benefits but also presents significant challenges when shifting to local but variable and power electronic-based sources, such as wind and solar. Island power systems are at the forefront of integrating extremely high shares of variable renewable generation and face related reliability challenges that are yet to be seen in larger interconnected power systems on the continental mainland. This is because island power systems operate in isolation and rely on their local grid resources to ensure stable operation.

Optimum operation management of microgrids with cost and environment pollution reduction approach considering uncertainty using multi‐objective NSGAII algorithm

AbstractGenerating electricity near the consumption can provide more flexibility to supply various services to consumers as well as reduce system losses. The limited fossil fuels and air pollution are among the main incentives for the expansion of this technology. One of the perspectives proposed for effective increase in these resources’ involvement is to combine these resources with the objectives of the visibility of the relationship between distributed generation resources and power network, as well as control of these resources more efficiently. One of the methods of combining distributed generation sources is a new concept called micro grid; a topic that is of great importance in this regard is the proper management of distributed generation resources in micro grid aimed to reduce the costs of generating electricity and polluting the environment. The resource management in micro grid is a completely non‐linear and top‐order problem. In this paper, micro grid optimum programming has been studied considering the capacities available in the electricity market. In order to realize this, as well as reduce pollution and cost simultaneously, NSGAII multi‐objective genetic algorithm is used. Studies have been conducted in the form of three scenarios on a sample micro grid consisting of solar, wind, micro turbine, fuel cell and battery resources, considering the uncertainty of load and solar and wind generations. The probabilistic distribution function (PDF) and Roulette Wheel were used to create variables with uncertainty. The first scenario objective was to reduce costs by PSO, GA, and ABC algorithms’ optimization. In the second scenario, pollution was reduced and the studies were repeated using these three algorithms. Finally, in the third scenario, there was a simultaneous reduction in pollution and cost by these three algorithms, taking into account weight coefficients as well as NSGAII algorithm.

Towards efficient and effective renewable energy prediction via deep learning

Renewable energy (RE) offers major environmental and economic benefits compared to nuclear and fuel-based energy; however, the data used for RE include significant randomness, intermittent behaviour, and strong-volatility, hindering their integration into smart grids. Accurate RE prediction is a promising solution to this problem and can provide effective planning and management services. Various predictive models have been developed to improve the prediction performance for better energy management. However, current works focus on improving the prediction accuracy, which is a requirement of power systems, without considering the time complexity of their methodologies. Considering these limitations, we develop a lightweight ESNCNN model for accurate RE prediction, in which an ESN learns the nonlinear mapping relationship and a CNN extracts the spatial information from RE data. The ESN and CNN layers are linearly connected via residual connections to avoid the vanishing gradient problem. Finally, we incorporate fully connected layers to enhance and select the optimal ESNCNN features to predict the future energy production. Our ESNCNN model is evaluated based on RE benchmarks, using various evaluation metrics such as MSE, MBE, MAE and RMSE, and achieves state-of-the-art performance. Further experiments are performed with different machine learning, deep learning, and hybrid models to select the optimum model. To fully assess the generalisation ability of the proposed ESNCNN, additional experiments are performed over electricity consumption datasets, which reveal an extensive decrease in error rates compared to other state-of-the-art approaches. Our model therefore represents a new paradigm for finding an energy equilibrium between grid and energy production resources using a single ESNCNN platform. Our results indicate a substantial reduction in the error rates over the RE dataset (MSE (5.01%), MAE (5.49%), and RMSE (3.76%)) and the electricity consumption dataset (MSE (5.37%), MAE (7.63%), RMSE (0.047%), and MBE (1.2%)).

Optimal coalition formation and maximum profit allocation for distributed energy resources in smart grids based on cooperative game theory

Over the past decades, significant revolutions have occurred on electricity market to reduce the electricity cost and increase profits. In particular, the novel structures facilitate the electricity manufacturers to participate in the market and earn more profit by cooperate with other producers. This paper presents a three-level gameplay-based intelligent structure to evaluate individual and collaborative strategies of electricity manufacturers, considering network and physical constraints. At the Level I, the particle swarm optimization (PSO) algorithm is implemented to determine the optimum power of distributed energy resources (DERs) in the power grid, to maximize the profits. Further, the fuzzy logic algorithm is applied to model the intermittent nature of the renewable sources and implement load demand in the power grid. At the Level II, DERs are classified into two different fuzzy logic groups to secure the fairness between every participant. Finally, at the Level III, the DERs in each group are combined each other by cooperative game theory-based algorithms to increase the coalition profits. Thereafter, Shapley, Nucleolus, and merge/split methods are applied to allocate a fair profit allocation by coalition formation. Ultimately, the results verify the proposed model influence electric players to find effective collaborative strategies under different conditions and environments.

Three-phase infeasibility analysis for distribution grid studies

With the increase of distributed energy resources in the distribution grid, planning to ensure sufficient infrastructure and resources becomes critical. Planning at the distribution level is limited by the complexities of optimizing unbalanced systems. In this paper we develop a three-phase infeasibility analysis that identifies weak locations in a distribution network. This optimization is formulated by adding slack current sources at nodes in the system and minimizing their norm subject to distribution power flow constraints. Through this analysis we solve instances of power flow that would otherwise be infeasible and diverge. Under conditions when power flow is feasible, our approach is equivalent to standard three-phase power flow; however, for cases where power flow fails, the nonzero slack injection currents compensate for missing power to make the grid feasible. Since an uncountable number of injected currents can provide feasibility, we further explore the optimization formulation that best fits the solution objective through use of both a least squares and an L1 norm objective. Our L1 norm formulation localizes power deficient locations through its inherent sparsity. We show the efficacy of this approach on realistic unbalanced testcases up to 8500 nodes and for a scenario with a high penetration of electric vehicles.

The water energy nexus: Improved emergency grid restoration with DERs

Water networks as critical infrastructures typically feature emergency electricity generators for bridging short power blackouts. We propose to combine these black start capable generators with available distributed energy resources (DERs) in the power grid, often photovoltaic generation, to jointly restore both the electricity and the water grid in the case of emergencies. This is mutually beneficial for both notworks since common grid-following inverters of DERs cannot supply power without a grid-forming nucleus. We model both grids as a coupled graph and formulate a stochastic mixed-integer linear program to determine optimal switch placement and/or optimal switching sequences jointly for both networks. Limited fuel and power availabilities, grid-forming constraints, storages, and an even distribution of available resources are considered. By minimizing the number of switching devices and switching events we target manual operability. The proposed method extends the time that can be bridged until a full restoration of the main power grid is achieved. For a small example, we demonstrate that given enough solar radiation our solution allows us to extend the water supply duration by a factor of two, compared to using the emergency generators only for the water network, while additionally almost half of the electricity demands can be resupplied. Algorithmic scaling is validated with a combination of the IEEE 123-bus test feeder and the D-Town water network.

Optimal Design Of Smart Grid Renewable Energy System Using Homer Programme

&lt;p&gt;Smart grid is a network created through information technology, communication technology and electrical power systems. It is simply a “smarter” power grid which ensures a two-way communication between user and the power supplier. This work proposes a smart microgrid design hybrid renewable energy system based on solar, grid and wind energy resources. The optimization has been performed using homer software programme to get the best and optimal operation system. Hybrid system combines several energy systems together which offer increased energy reliability and security, and carry a large economic opportunity in terms of cost saving. A typical microgrid system would also include intelligent management that interfaces with the equipment via wired or wireless communication protocols. In the present work the optimization model has been developed for the optimal operation of the system. The modelled system collects meteorological and load data from a town. The optimal hybrid system design is realized by satisfying the load demand, nonlinear seasonal variations and equipment constraints. The main focus of the system is on operation of smart microgrids to maximize usage of green energy, reduction of environmental emissions, decrement of levelized cost of electricity and intelligent management of overall system.&lt;/p&gt;