Grid Resources Research Articles

Regulatory Implications for considering flexible resources in network expansion planning: main lessons from regional cases in the H2020 FlexPlan project

The FlexPlan Horizon2020 project aimed at establishing a new grid planning methodology which considers the opportunity to introduce new storage and flexibility resources in electricity transmission and distribution grids as an alternative to building new grid elements, in accordance with the intentions of the European Commission regulatory package "Clean Energy for all Europeans". FlexPlan creates a new innovative grid planning tool which is intended to go beyond the state of the art of planning methodologies by including the following innovative features: assessment of best planning strategy by analysing in one shot a high number of candidate expansion options provided by a pre-processor tool, simultaneous mid- and long-term planning assessment over three grid years (2030-2040-2050), incorporation of full range of cost benefit analysis criteria into the target function, integrated transmission distribution planning, embedded environmental analysis (air quality, carbon footprint, landscape constraints), probabilistic contingency methodologies in replacement of the traditional N-1 criterion, application of numerical decomposition techniques to reduce calculation efforts and analysis of variability of yearly RES and load time series through a Monte Carlo process. Six regional cases covering nearly the whole European continent are developed in order to cast a view on grid planning in Europe till 2050. The final step in FlexPlan was formulating guidelines for regulators and planning offices of system operators, by indicating to what extent system flexibility can contribute to reduce overall system costs (operational and investment) yet maintaining current system security levels and which regulatory provisions could foster such process. This paper focuses on the regulatory issues, which were uncovered during the initiation phase and of the project and refined in throughout six regional cases. In order to substantiate this, the paper explains in brief the developed and applied FlexPlan methodology and its testing in six regional cases.

Preface

The 2023 2nd International Conference on New Energy, Energy Storage and Power Engineering (NESP 2023), held from April 21st to 23rd, 2023 in Kaifeng, China (virtual event), is a premier academic conference on new energy, energy storage and power engineering for research individuals and groups working on related fields. It allows researchers to share their experiences with other groups from all over the world by presenting scientific work and potential innovations in this research area and also in the social events promoting informal get togethers.The works cover a broad range of fields, including Electrochemical Energy Storage Device, Lithium Sulfur Batteries, Energy Storage Material, Power Transmission, Power Quality Protection and Electromagnetic Compatibility, etc. Also, the Conference is intended to give students and research groups the opportunity to learn more about new energy, energy storage and power engineering as an important tool in their applications and development.The Conference is designed to showcase cutting-edge research and accomplishments, and to enrich the educational and industrial experience in this field. It also represents a unique opportunity for all the participants to meet colleagues and friends, exchanging ideas, and learning about new developments, while working to advance the understanding of the knowledge base that we will collectively draw upon in the years ahead to meet future challenges. Participants will attend presentations by scholars representing both institutes and academia.The NESP 2023 Proceedings include about 110 papers selected via a peer review process. The Conference Program features keynote speeches by eight world-leading scholars and many sessions for regular paper oral presentations, attracting around 150 participants. Among them, Professor Pierluigi Siano from University of Salerno, Italy gave a speech. His research activities are centered on demand response, on the integration of distributed energy resources in smart grids and on planning and management of power systems. He has co-authored more than 450 papers including more than 200 international journal papers that received more than 7600 citations with an H-index equal to 44.In making this Conference possible, we want to acknowledge our deep appreciation for the support of Murdoch University, Australia and Universiti Tunku Abdul Rahman, Malaysia. And we would like to extend an enthusiastic round of thanks to all our conference authors for their excellent contributions; to all the session Chairs for their effort and enthusiasm; and to all the Technical Program Committee members and referees for their time and expertise in the paper reviewing process. Particular acknowledgement to the staff of Journal of Physics: Conference Series for their time and outstanding publishing work.The Committee of NESP 2023List of Committee Member are available in this pdf.

Accelerating Distributed Optimization via Over-the-Air Computing

Distributed optimization is ubiquitous in emerging applications, such as robust sensor network control, smart grid management, machine learning, resource slicing, and localization. However, the extensive data exchange among local and central nodes may cause a severe communication bottleneck. To overcome this challenge, over-the-air computing (AirComp) is a promising medium access technology, which exploits the superposition property of the wireless multiple access channel (MAC) and offers significant bandwidth savings. In this work, we propose an AirComp framework for general distributed convex optimization problems. Specifically, a distributed primal-dual (DPD) subgradient method is utilized for the optimization procedure. Under general assumptions, we prove that DPD-AirComp can asymptotically achieve zero expected constraint violation. Therefore, DPD-AirComp ensures the feasibility of the original problem, despite the presence of channel fading and additive noise. Moreover, with proper power control of the users’ signals, the expected non-zero optimality gap can also be mitigated. Two practical applications of the proposed framework are presented, namely, smart grid management and wireless resource allocation. Finally, numerical results confirm DPD-AirComp’s excellent performance, while it is also shown that DPD-AirComp converges an order of magnitude faster compared to two digital orthogonal multiple access schemes, specifically, time-division multiple access (TDMA), and orthogonal frequency-division multiple access (OFDMA).

Coordinated wind power plant and battery control for active power services

This article considers joint active power control of wind turbines and battery storage to follow a plant-level power reference signal. The joint control dynamically curtails the energy from a subset of the wind turbines and stores or withdraws energy from the battery to meet the power reference setpoint while accounting for wind plant aerodynamic interactions, such as wake losses. As a use case, we study the performance of the controller in maintaining a constant power output over hourly periods. A wind plant operating in this way would rely much less on other grid resources to meet its contractual agreements, thereby improving grid reliability, especially in grids with high penetration of wind and solar generation. We compare the operation of the wind plant under joint active power control to standard power-maximizing control with battery support. We present an analysis of the performance of the control system architecture. To study the impact of the battery size on performance, we simulate a 50-MW wind plant supported by batteries ranging from 8 to 64 MWh. We then evaluate the over and undergeneration penalties incurred by the plant during the simulation period.

Research on a random search algorithm for wind turbine layout optimization

Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search.

Potential Impacts of Climate Change on Renewable Energy and Storage Requirements for Grid Reliability and Resource Adequacy

Abstract This paper provides a study of the potential impacts of climate change on intermittent renewable energy resources and storage requirements for grid reliability and resource adequacy. Climate change models and available regional data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed within a specific U.S. southwest service area as a case study. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources. Results for the extreme climate change scenario show that the projected wind power may decrease by ∼13% due to projected decreases in wind speed. Projected solar power may decrease by ∼4% due to decreases in irradiance and increases in temperature. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance among electrical load, generation, and storage was calculated throughout the year. The annual loss of load expectation (LOLE) was found to increase from ∼0 days/year to a median value of ∼2 days/year due to potential reductions in renewable energy resources caused by climate change and decreased battery performance. Significantly increased battery storage was required to reduce the LOLE to desired values of 0.2 days/year.

SOLAR ENERGY FORECASTING WITH DEEP LEARNING TECHNIQUE

The increasing reliance on renewable energy sources, such as solar power, necessitates accurate forecasting to ensure efficient grid integration and stability. This study explores the application of deep learning techniques, particularly deep neural networks (DNNs), in predicting solar irradiance and power output. By leveraging advanced algorithms and large datasets, this research aims to enhance the precision of solar energy forecasts, thereby optimizing grid management and resource allocation. Deep learning techniques, characterized by their ability to model complex nonlinear relationships, offer significant advantages over traditional statistical methods in forecasting solar energy. This study focuses on the development and evaluation of deep neural networks for predicting solar irradiance, which directly influences the power output of photovoltaic (PV) systems. The models are trained on extensive historical weather data, satellite imagery, and real-time solar output measurements to capture temporal and spatial variations in solar energy. Key components of the research include data preprocessing to handle missing values and noise, feature extraction to identify relevant patterns, and model training using various architectures of DNNs such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The study also employs ensemble learning techniques to combine predictions from multiple models, further enhancing forecast accuracy. Results demonstrate that deep neural networks significantly outperform traditional forecasting methods in terms of accuracy and reliability. The improved forecasts enable better scheduling of energy generation and distribution, reducing reliance on fossil fuels and minimizing grid imbalances. Additionally, accurate solar energy predictions support the efficient integration of solar power into the grid, enhancing overall system stability and reducing operational costs. The study also addresses challenges in implementing deep learning models, such as computational requirements and the need for large, high-quality datasets. Solutions include leveraging cloud computing resources and developing standardized data collection protocols to facilitate broader application and scalability of the models. Application of deep learning techniques in solar energy forecasting represents a promising advancement for the renewable energy sector. By improving the accuracy of solar irradiance and power output predictions, deep neural networks contribute to more reliable grid integration and efficient energy management. This research advocates for the continued exploration and adoption of advanced machine learning methods to support the transition to a sustainable energy future. Keywords: Deep Learning Techniques; Solar Energy; Forecasting; Grid Integration; Power Output.

A flexible OT testbed for evaluating on-device implementations of IEC-61850 GOOSE

The growing convergence of Information Technology and Operational Technology has enhanced communication and visibility across power grids. This, coupled with the growing use of Distributed Energy Resources in power grids, has enhanced the grid capabilities while also creating a larger attack surface for malicious actors. A common protocol vulnerable to these attacks is the IEC-61850 GOOSE protocol due to its low-latency requirements, multicast packet delivery method, and lack of encryption. In this paper, we evaluate the security implications of different hardware implementations of this protocol by contrasting device response and recovery of two commercial off-the-shelf Intelligent Electronic Devices from separate manufacturers. The cyberattacks utilized in this paper are research-established GOOSE attacks with results measured in device latency and GOOSE endpoint response success.

SNR Gain Evaluation in Narrowband IoT Uplink Data Transmission with Repetition Increment: A Simulation Approach

Deploying Internet of Things (IoT) on a large scale necessitates widespread network infrastructures supporting Machine Type Communication. Integrating IoT into cellular networks like LTE, known as Narrowband-IoT (NB-IoT), can fulfill this infrastructure need. Standard 3GPP Release 13 introduces NB-IoT's Repetition features, expanding radio transmission coverage while maintaining LTE performance. Focusing on uplink data traffic, this study examines NB-IoT's repetition mechanism, grid resource distribution, and NPUSCH performance through simulations. Results show that at SNR greater than -5 dB, maximum repetitions of 128 yield the highest BLER, while minimum repetitions of 2 result in the lowest. Quadrupling repetitions increases SNR by 5 dB, emphasizing repetition's role in error mitigation and uplink reliability, especially in challenging SNR conditions. For optimal throughput in SNR above -5 dB, maximum repetitions of 128 for NPUSCH format 1 are recommended. These findings underscore the importance of repetition in enhancing Narrowband IoT performance, offering insights for system optimization, where increasing the number of repetitions generally leads to higher SNR gain. The attained BLER and throughput values from Narrowband IoT simulations highlight the robustness of data transmission across varying channel conditions, affirming NB-IoT applicability to a wide range of IoT applications.

Sensor and Actuator Fault Tolerant Control of Grid-Tied Microgrid

The increasing penetration of the distributed energy resources (DERs) in the electrical grid introduces new challenges in terms of grid’s reliable operation. DERs are connected to the main network at the distribution level, possibly through the microgrid, at the point of common coupling (PCC). Microgrids have often the capability of operation in both grid-tied and islanded modes. In the islanded mode, the generated active and reactive powers must be in equilibrium with the load demand, while in the grid-tied mode, the active and active powers follow the desired references defined by the distribution system operator. Therefore, in both cases, proper control is crucial for the operation of the microgrid and its DERs. In this paper, a fault tolerant control (FTC) method based on H∞ observer is proposed for a DER supplying a grid-tied microgrid to make it resilient against sensor and actuator faults and guarantee its reliable operation.

Loss Modeling of Large Hydrogenerators for Cost Estimation of Reactive Power Services and Identification of Optimal Operation

As a result of the worldwide energy transition, reactive power generation has started to become a more scarce resource in the power grid. Until recently, reactive power has been an auxiliary grid service that classical power generation facilities have provided without necessarily allocating any cost for this valuable service. In this paper, a new approach for predicting the additional costs of reactive power services delivered by large hydrogenerators is proposed. We derive the optimal reactive power (ORP) with minimal losses as a function of the active power level within the generator's capability diagram. This pathway can then be used to calculate additional losses from operational regimes deviating from the ORP. To back up the analysis, a dedicated example study was handpicked consisting of four real-world generators scaled in terms of power rating, i.e., 15MVA, 47MVA, 103MVA, and 160MVA. The objective was to identify how the ORP scale from smaller to larger MVAsized generators. Moreover, a sensitivity analysis of the machine characteristics is conducted. We find the ratio between the rotor and stator losses as the determining factor. Finally, we show how our framework could justify profit for reactive power services, which are projected to increase in the future.

Impact of the Size of Data on the Reliability of Short-Term Load Forecasting Using LSTM and GRU

Load forecasting has been an important aspect of power system operations and with the increase in integration of renewable energy resources in the main grid, the procedure is now more vital than ever. The methods developed to forecast the load of an area have also been improved with the use of artificial intelligence. This study proposes a forecasting training method using Gated Recurrent Units and compares it with the most widely used long-short term memory. The test systems are made of the historical load data from publicly available load data through PJM data miner 2 without the inclusion of weather parameters which reduces the training time of the models along with the reduction in data acquisition cost. The study also considers the impact of predicting future load without access to weather data.

Adaptive distributed scheduling of the resources in smart grids with considering demand response

Unit commitment (UC), a non-linear and non-convex problem, is one of the important problems in power system operation. Solving the UC problem centrally has problems such as security concerns. On the contrary, solving it in a fully distributed manner reduces performance speed. This paper presents a method for solving the UC problem that addresses both security concerns and speeds up performance. This paper intends to solve the UC problem by using a hybrid structure and with the help of accelerator and cyber security parameters of the power system. This problem is solved for the first time in a power system despite its non-linear nature and non-convexity. Also, the demand response (DR) issue is considered as a contributing factor in resource scheduling management. This is important because DR is an inherently distributed issue. The presented method uses a developed decentralized algorithm called the accelerated hybrid alternating direction method of multipliers (AHADMM). AHADMM is a combination of the ADMM framework and hybrid network architecture that its parameters are tuned. Then for decentralized optimization, the generalized Benders decomposition (GBD) algorithm and the AHADMM algorithm are utilized to speed up the computation and provide a framework to exploit the distributed nature of the problem. The proposed algorithm consists of two main problems, primal and master problems. The primal problem, which is solved in a distributed way, is responsible for providing a feasible solution such that an optimality cut can be generated. In contrast, the master problem is responsible for accumulating the optimality cuts and approximating the feasible region of the original non-linear problem. Regarding the improvement of the solution method, we have used the accelerator parameters and the limitation parameter of fusion centers in such a way as to guarantee the speed of calculations and the cyber security of the power system.

Fish response to the presence of hydrokinetic turbines as a sustainable energy solution

Hydrokinetic turbines such as vertical axis turbines (VATs) may provide decentralised, clean, sustainable energy for remote communities that lack access to the main energy grid or renewable resources. As traditional hydropower adversely alters aquatic ecosystems, it is essential to evaluate the environmental consequences of deploying VATs in riverine ecosystems to meet current and future energy needs. This study explores the implications of VATs on fish movement by observing fish swimming behaviour under two discharges, turbine operation states, and cross-sections confinements using scaled laboratory experiments. Our findings reveal that for cross-sectional confined conditions neither discharge, turbine presence, nor device operation, prevented fish from passing around and through the turbine both in the up- and downstream directions. However, fish spent the least time near the turbine vicinity and within the turbine’s turbulent, low-velocity wake, indicating avoidance behaviour. Swimming in a less confined test section further reduced the time spent within the turbine’s vicinity and wake, increasing the distance fish kept away from the device. Our results contribute to an understanding of VATs as low-risk hazards for fish swimming behaviour, advancing the potential of deploying VATs in rivers, estuaries or sea as a renewable energy solution for remote communities.

Performance Analysis of a Hybrid Renewable-Energy System for Green Buildings to Improve Efficiency and Reduce GHG Emissions with Multiple Scenarios

A hybrid system, such as solar and wind, may be more successful than nonhybrid systems in accelerating the transition from conventional to renewable power sources. However, these new energy sources have several challenges, such as intermittency, storage capacity, and grid stability. This paper presents a complete analysis and study of a hybrid renewable-energy system (HRES) to convert a facility into a green building and reduce its dependence on conventional energy by generating clean energy with near-zero greenhouse-gas (GHG) emissions. The proposed system aims to reduce the energy bill of a hotel in Petra, Jordan, by considering different sustainable energy resource configurations in a grid-connected hybrid renewable energy system (GHRES). The hybrid optimization of multiple energy resources (HOMER) grid software was utilized on the hybrid systems to study ways to improve their overall efficiency and mitigate GHG emissions from an economic perspective. The hybrid system components included in the simulation were a solar photovoltaic (PV) system, a wind turbine (WT) system, a diesel generator (DG), and a converter. Five scenarios (PV–Converter–DG–Grid, PV–Converter–Battery–DG–Grid, WT–DG–Grid, PV–WT–Converter–Battery–DG–Grid, PV–WT–Converter–DG–Grid) were considered. The optimal configuration had a USD 1.16 M total net present cost, USD 0.0415/kWh cost of energy, 15.8% effective internal rate of return, and an approximately 77% reduction in carbon emissions compared to the base case.

Research on the Optimal Capacity Configuration Method of Park-type Wind-photovoltaic Storage Complementary Power Generation System

At present, China’s installed renewable energy capacity is growing at a fast rate, and reasonable allocation of the wind turbine, photovoltaic, and energy storage capacity is a prerequisite to ensure the economic and reliable operation of the system. For the park-type grid-connected operation of the wind-photovoltaic storage system, in order to improve the stability of the system and make full use of the distribution grid resources, the operation strategy of the park-type wind-photovoltaic storage system is proposed, and the model of wind-photovoltaic storage is established respectively, and the economy and reliability of the system are considered comprehensively. The objective function of capacity configuration and the constraints of power system operation specification are set up, combined with the time-of-use electricity price, the charging of energy storage battery is preferred at the low valley, and the objective function is solved by a genetic algorithm according to the energy use characteristics of the park, and three configuration schemes are selected from the Pareto front solution. Finally, through the typical day analysis, the results show that the wind-photovoltaic storage capacity allocation method can effectively improve the economy of park operation and the quality of the park power supply.

Data Storage Optimization Model Based on Improved Simulated Annealing Algorithm

Since there is a longitudinal and horizontal penetration problem between multi-level data centers in the smart grid information transmission network. Based on the improved Simulated Annealing algorithm, this paper proposes a data storage optimization model for smart grids based on Hadoop architecture. Combining the characteristics of distributed storage in cloud computing, the smart grid data are equivalent to a task-oriented data set. The smart grid information platform is flattened, equal to a collection of multiple distributed data centers. The smart grid data over time were counted to derive the dependencies between task sets and data sets. According to the dependency between task sets and data sets, the mathematical model was established in combination with the actual data transmission of the power grid. The optimal transmission correspondence between each data set and the data center was calculated. An improved Simulated Annealing algorithm solves the longitudinal and horizontal penetration problem between multi-level data centers. When generating a new solution, the Grey Wolf algorithm provides direction for finding the optimal solution. This paper integrated the existing business data and computational storage resources in the smart grid to establish a mathematical model of the affiliation between data centers and data sets. The optimal distribution of the data set was calculated, and the optimally distributed data set was stored in a distributed physical disk. Arithmetic examples were used to analyze the efficiency and stability of several algorithms to verify the improved algorithm’s advantages, and the improved algorithms’ effectiveness was confirmed by simulation.

Peer to Peer Distributed Solar Energy Trading

Solar module prices have dramatically dropped in recent years, which in turn has facilitated distributed solar energy resources (DSERs) in smart grids. To manage electricity real time supply and demand, the utilities has shown the strong interest in deploying virtual power plants(VPPs) which enable solar generated energy trading to reduce the intermittent DSERs impact in electric grid. Unfortunately, the current energy trading approaches in residential VPPs typically require a trusted third party to take on the role of the middleman, DSER users are not allowed to trade their surplus solar energy independently and simultaneously to maximize potential benefits. In addition, these approaches do not achieve "fair" for both VPPs and DSERs users. To address this issue, we build a multi-agent deep reinforcement learning based peer to peer (P2P) new solar energy trading system-SolarTrader+, which enables unsupervised, distributed, and long term fair solar energy trading in residential VPPs. We apply deep reinforcement learning with neural networks as Q-value function approximator.We implement SolarTrader+ and evaluate it using data from U.S. residential VPP communities that are comprised of - 119 residential DSERs. Our results show that SolarTrader+ can reduce the aggregated VPP energy consumption by 83.8% when compared against a non-trading approach.

Grid‐aware provision and activation of fast and slow power flexibilities from distributed resources in low and medium voltage grids

AbstractThe intermittencies of renewable generations give rise to situations, which require both slow‐ and fast‐ramping flexibility capability from a variety of resources connected at the medium voltage (MV) andlow voltage (LV) distribution grids. Moreover, this may increase the grid reinforcement costs. To defer this reinforcement, the grid needs to be operated optimally. This paper proposes—(a) such an optimal operational methodology forthe MV and LV grids; and (b) an aggregated flexibility estimation methodology separately for fast and slow services at the primary substation (transmission interface). The methodologies based on model‐based MV grids and sensitivity coefficients‐based LV grids are suitable for LV grids where an up‐to‐date, accurate model and topology are not always available. Besides, the costs and resources associated to create full LV grid model and visibility should be prevented. The approaches, here, use the synchronised and accurate measurements from grid monitoring devices located at the LV distribution grids. They have been validated on a real MV and LV networks of a Swiss distribution grid operator equipped with such devices. The results, in terms of reducing technical losses, reducing grid violation costs,and estimating flexibility capability show the efficacy of the proposed methodologies and therefore, can be easily deployed.

Energy Management in a Standalone Microgrid: A Split-Horizon Dual-Stage Dispatch Strategy

Microgrid technology has recently gained global attention over increasing demands for the inclusion of renewable energy resources in power grids, requiring constant research and development in aspects such as control, protection, reliability, and management. With an ever-increasing scope for maximizing renewable energy output, there is also a need to reduce the curtailment of power on both the generation and demand sides by increasing forecasting accuracies and using resources more effectively. This paper proposes a dual-stage dispatch employing a novel “split-horizon” strategy, in a bid to enhance energy management in a standalone microgrid. The split-horizon is essentially the considered time horizon split into equal operational periods of the dual-stage dispatch. The proposed strategy utilizes a custom-designed novel variant of the inertia-weight-based particle swarm optimization (PSO), termed customized PSO, to perform the optimal schedule and dispatch operation by benefitting from the simplicity of PSO and customization as per the considered objectives. A modified IEEE 34-node test system is derived into a standalone microgrid with added distributed energy resources to test the proposed strategy, while another standalone microgrid, a modified IEEE 69-node test feeder, is also considered for scalability. Furthermore, the validation of the strategy is performed appropriately with a case study while also validating the proposed optimization algorithm. It is observed that the proposed energy management strategy provides approximatelya 7% reduction in costs.