Grid Scenario Research Articles

Method of identification of transient instability mode based on the ratio of generators transmission power limitations to rated capacities

Transient angle instability and transient voltage instability are usually mixed and coupled in transient instability scenarios in complex grid. In this context, this paper provides a quantitative analysis method of the identification of dominant mode of transient instability in complex grid based on the ratio of generator transmission power limitation to its spinning capacity (TPLR). Differently from most existing work that analyzes the mechanism on the dominant mode of transient instability through direct method such as characteristic root which has low applicability for analyzing, an assessment method is conducted here through time-series simulation that specifically considers the limitation of transmission power of key generator under different installed scale. The transient instability mode can be identified based on the curve of TPLR, i.e. the ratio of transmission power limitation to the spinning capacity. The method is demonstrated on a complex transmission network with transient voltage instability and transient angle instability scenarios. The simulation results help verify the accuracy and applicability of the provided transient instability mode identification method.

Building flexibility revenue in modeled future bulk power systems with varying levels of renewable energy

The buildings sector can provide important demand-side flexibility for the integration of renewable energy. With the changing power system and rapid advancement in building energy control technology, technology providers and demand response aggregators need to know what potential revenues could be obtained by providing grid services. We dispatch a normalized and parameterized model of building load shifting against marginal service prices from grid investment and operational models to produce a database of the capacity, energy, and ancillary service revenues (gross value of providing bulk power system services) for a marginal kilowatt-hour (kWh) of shiftable load. The database is geographically disaggregated, hourly, and parameterized so that flexibility value can be estimated for a wide range of building technologies. The database covers the contiguous United States under three 2030 grid scenarios. Given the high degree of uncertainty in such grid projections, the results are perhaps best interpreted in terms of regional climate and grid mixes and are thus potentially applicable in non-U.S. contexts. Upon analyzing the results, we find the monthly mean gross value of building load shifting is 0–38 cents/kWh-day and the daily gross value of shifting the highest-value hour each day can be up to 620 cents/kWh-day. The different revenues obtained by aggregating results in different ways, as well as observed regional and seasonal differences, suggest different building technologies and grid environments might call for demand response program designs and business models.

Flexible energy storage power station with dual functions of power flow regulation and energy storage based on energy-sharing concept

The high proportion of renewable energy access and randomness of load side has resulted in several operational challenges for conventional power systems. Firstly, this paper proposes the concept of a flexible energy storage power station (FESPS) on the basis of an energy-sharing concept, which offers the dual functions of power flow regulation and energy storage. Moreover, the real-time application scenarios, operation, and implementation process for the FESPS have been analyzed herein. Subsequently, a method involving a bilevel optimization model was adopted: by replacing the original energy storage capacity at each end of the source, grid, and load with the FESPS, the energy storage capacity was fully released. Finally, a case study was performed to verify that the proposed FESPS based on the energy-sharing concept can effectively promote the on-site consumption of renewable energy, reduce the configuration capacity of energy storage equipment, and realize the function of energy storage/reuse. Notably, the application of FESPS in different application scenarios of the power grid is conducive to promoting the construction of new power systems. Configuration capacity of FESPS is only 70% of that of conventional power stations featuring shared energy storage.

Impact of wind speed distribution and management strategy on hydrogen production from wind energy

Electrolytic hydrogen production using renewable sources can play a central role in global decarbonization. However, the direct coupling of electrolyzers with renewable energies can cause frequent shutdowns and high production fluctuations unless adopting electrical storage systems or using electricity from the grid that is generally not completely decarbonized. In this study, a system consisting of a wind turbine, short-term battery storage, and an alkaline electrolyzer was analyzed through annual simulations in MATLAB. A power management strategy sets the electrolyzer and battery operating conditions. Firstly, several battery and electrolyzer sizes were investigated for a given wind site for three scenarios (0%grid, 20%grid, and 100%grid) in which the grid guarantees the electrolyzer operation at a minimum load of 0%, 20%, and 100%, respectively. Secondly, the effect of the wind speed distribution on the system performance was investigated by comparing sixteen different wind sites in the 0%grid scenario. The battery presence always led to a LCOH increase. The 20%grid scenario represented a good compromise between the minimization of specific CO2 emissions, and the minimization of LCOH (that was always lower compared to the 0%grid scenario and the lowest without battery). The shape parameter of the fitted Weibull wind speed distribution did not affect the system. Instead, a greater scale parameter led to both greater hydrogen production and a lower LCOH.

Decentralised Multi-Agent Reinforcement Learning Approach for the Same-Day Delivery Problem

Same-day delivery (SDD) services have become increasingly popular in recent years. These have been usually modeled by previous studies as a certain class of dynamic vehicle routing problem (DVRP) where goods must be delivered from a depot to a set of customers in the same day that the orders were placed. Adaptive exact solution methods for DVRPs can become intractable even for small problem instances. In this paper, the same-day delivery problem (SDDP) is formulated as a Markov decision process (MDP) and it is solved using a parameter-sharing Deep Q-Network, which corresponds to a decentralised multi-agent reinforcement learning (MARL) approach. For this, a multi-agent grid-based SDD environment is created, consisting of multiple vehicles, a central depot, and dynamic order generation. In addition, zone-specific order generation and reward probabilities are introduced. The performance of the proposed MARL approach is compared against a mixed-integer programming (MIP) solution. Results show that the proposed MARL framework performs on par with MIP-based policy when the number of orders is relatively low. For problem instances with higher order arrival rates, computational results show that the MARL approach underperforms MIP by up to 30%. The performance gap between both methods becomes smaller when zone-specific parameters are employed. The gap is reduced from 30% to 3% for a 5 × 5 grid scenario with 30 orders. Execution time results indicate that the MARL approach is, on average, 65 times faster than the MIP-based policy, and therefore may be more advantageous for real-time control, at least for small-sized instances.

Combination of market-oriented and grid-serving flexibility usage for private ev charging points

Due to the increasing market penetration of electric vehicles, more critical grid states may occur in the distribution grids, which could be solved utiliting the flexibility of the EVs charging processes. At times when no grid-serving usage is necessary, the flexibility may also be used for market-oriented procurement optimisation. This paper combines such a strategy with different incentive models for the grid-serving flexibility usage of private charging points, especially local flexibility markets and a quotation of the charging power, and compares the profitability for the different actors for grid scenarios in 2035 and 2050. Additionally to the profit of the aggregator, the perspective of the EV owners, who can save up to 18% of their electricity costs in 2035, is shown. In order to highlight the DSO perspective, his operational flexibility costs and the number of critical grid states are presented for the different options and years. It turns out that the local flexibility markets are much more expensive than the quotation for the considered low-voltage grid, due to the high quantity of critical grid states, especially in 2050.

On the operational characteristics and economic value of pumped thermal energy storage

Pumped thermal energy storage (PTES) systems use an electrically-driven heat pump to store electricity in the form of thermal energy, and subsequently dispatch the stored thermal energy to generate electricity using a thermodynamic heat engine. Optimal day-ahead operational scheduling and annual value of a PTES system based on Joule–Brayton thermodynamic cycles and two-tank molten salt hot thermal storage is evaluated in this paper. Production cost models, which simultaneously optimize commitment and dispatch schedules for an entire set of generators to minimize the cost of satisfying electricity demand, are employed to determine system-optimal operation and day-ahead energy value of the PTES system within each of six hypothetical near-future grid scenarios intended to approximately represent the U.S. Western Interconnection or the Texas Interconnection. Sensitivity to grid scenario (including the contribution of variable renewable energy sources), thermal storage capacity, relative heat pump and heat engine capacities, and startup/shutdown cycling costs are evaluated. PTES energy value and heat engine annual capacity factor increase strongly as the contribution of variable renewable resources increases, heat pump capacity increases relative to heat engine capacity, or PTES cycling costs decrease. Grid scenarios in which the contribution of variable renewable energy is dominated by solar photovoltaics (PV) vs. wind produce inherently different PTES operational patterns. Annual PTES energy value within PV-dominated scenarios increased with storage capacity only up to approximately seven hours of full-load discharge capacity, whereas that within wind-dominated scenarios exhibited a continual increase with storage duration up to at least 16 hours.

Chinese Named Entity Recognition Based on Knowledge Based Question Answering System

The KBQA (Knowledge-Based Question Answering) system is an essential part of the smart customer service system. KBQA is a type of QA (Question Answering) system based on KB (Knowledge Base). It aims to automatically answer natural language questions by retrieving structured data stored in the knowledge base. Generally, when a KBQA system receives the user’s query, it first needs to recognize topic entities of the query, such as name, location, organization, etc. This process is the NER (Named Entity Recognition). In this paper, we use the Bidirectional Long Short-Term Memory-Conditional Random Field (Bi-LSTM-CRF) model and introduce the SoftLexicon method for a Chinese NER task. At the same time, according to the analysis of the characteristics of application scenario, we propose a fuzzy matching module based on the combination of multiple methods. This module can efficiently modify the error recognition results, which can further improve the performance of entity recognition. We combine the NER model and the fuzzy matching module into an NER system. To explore the availability of the system in some specific fields, such as a power grid field, we utilize the power grid-related original data collected by the Hebei Electric Power Company to improve our system according to the characteristics of data in the power grid field. We innovatively make the dataset and high-frequency word lexicon in the power grid field, which makes our proposed NER system perform better in recognizing entities in the field of power grid. We used the cross-validation method for validation. The experimental results show that the F1-score of the improved NER model on the power grid dataset reaches 92.43%. After processing the recognition results by using the fuzzy matching module, about 99% of the entities in the test set can be correctly recognized. It proves that the proposed NER system can achieve excellent performance in the application scenario of a power grid. The results of this work will also fill the gap in the research of intelligent customer-service-related technologies in the power grid field in China.

Threshold-free localized scheme for DC fault identification in multiterminal HVDC systems

In the future grid scenario, the continuous operation of large-capacity remote power transmission over an augmented DC network is imperative for implementing a reliable multi-terminal high voltage direct current (MT-HVDC) grid or, alternatively, an MTDC grid. A fast and selective DC fault identification method with improved reliability eventually enhances the operational continuity and flexibility of the MTDC grid. Based on the V/I ratio of a current limiting reactor, this paper proposes a new non-unit protection scheme for DC fault identification without a threshold value setting. The DC fault is precisely identified in a span of three sampling intervals by the V/I ratios of four sampling instants. The selectivity of the proposed scheme is verified in a four-terminal MTDC grid. Simulation results demonstrate that the effective detection, discrimination, and location of a DC fault strictly satisfies the speed requirement (<1ms) with a location error of less than 1%. Moreover, the relevant sensitivity analyses reveal that the proposed scheme remains invariant for a wide range of fault locations and resistances and perform efficiently, even with a lower sampling frequency. In addition, transient events apart from DC faults fail to trigger the protection, indicating the high robustness of the proposed scheme.

Plug-in Electric Vehicles Participation in Primary Frequency Control Considering Charging Demand

ABSTRACT Electric Vehicle is now a global term, affecting the transportation sector, automobile industry, and electric grid scenario. As Plug-in Electric Vehicles (PEVs) penetration level is escalating, PEVs will create new challenges and opportunities for transportation and the electric grid in the near future. The distributed stored energy of PEVs with high active power controllability turns PEVs into new distributed energy sources (DES) for the grid. With a high penetration level, PEVs can also provide ancillary services to the grid, e.g., primary frequency control (PFC). Although grid support provided by PEVs is limited because of additional operational limitations and various constraints of PEVs and the grid. A new charging model for PEVs has been proposed in this paper to investigate the potential of PEVs for primary frequency control. The proposed model incorporates several PEV characteristics such as charger topology, drive power limitation, and operational modes with proper consideration of PEV’s charging demand profile. Furthermore, a control scheme is proposed to decide the availability of PEVs for ancillary services and compute the participation level of PEVs without affecting the charging demands. In the end, several scenarios have been used to evaluate and show the potential of PEVs for the PFC in the power system. The maximum deviation of frequency is reduced by 17.39% in case 1 of Scenario A, 26.08% in case 2 of Scenario A and 21.73% in both cases of Scenario B with proper consideration of charging demands.

Role of On-Site Generation in Carbon Emissions and Utility Bill Savings under Different Electric Grid Scenarios

Energy-efficient and sustainable technologies are necessary to lower energy and carbon footprints. Many technologies are being pursued to meet the increasing energy demand in buildings. An attractive option is efficient utilization of available energy resources, including renewables, to support current and future building energy needs while targeting grid resiliency, energy, and environmental security at an affordable cost via on-site cogeneration-based approaches. This must include energy-efficient technologies with lower greenhouse gas emissions and optimized cost, performance, and reliability. This paper presents the economic and environmental benefits associated with power technologies such as thermionics and solid oxide fuel cells. Hybrid configurations consisting of heat pumps, power systems, and renewable photovoltaics in cogeneration and trigeneration modes of operation are presented. The role of such technologies in lowering CO2 emissions while improving energy resiliency and serving the needs of underprivileged communities is discussed. The key barriers of affordability and potential solutions for large-scale implementation of these promising technologies are reviewed. Case studies demonstrating the influence of power rating, electrical efficiency, design configuration, carbon dioxide intensity of the grid, and fuel on annual greenhouse gas emissions are presented for residential and commercial buildings.

Stability and Accuracy Analysis of a Distributed Digital Real-Time Cosimulation Infrastructure

Cosimulation techniques are gaining popularity amongst the power system research community to analyze future scalable smart grid solutions. However, complications such as multiple communication protocols, uncertainty in latencies are holding up the widespread usage of these techniques for the power system analysis. These issues are even further exacerbated when applied to digital real-time simulators (DRTSs) with strict real-time constraints for power hardware-in-the-loop (PHIL) tests. In this article, we present an innovative digital real-time cosimulation infrastructure that allows interconnecting different DRTS through the Aurora 8B/10B protocol to reduce the effects of communication latency and respect real-time constraints. The proposed solution synchronizes the DRTS interconnection by means of the IEEE 1588 precision time protocol (PTP) standard to align executions and results obtained by the cosimulated scenario. The ideal transformer method interface algorithm, commonly used in PHIL applications, is used to interface the DRTS. Finally, we present time-domain and frequency-domain accuracy analyzes on the obtained experimental results to demonstrate the potential of the proposed infrastructure. With the presented setup, a time step duration down to 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s is shown to be stable and accurate in running an electromagnetic transients cosimulated power grid scenario by interconnecting two commercial DRTS (i.e., RTDS NovaCor), extending the scalability of future smart grid real-time simulations.

A Robust Exciter Controller Design for Synchronous Condensers in Weak Grids

Weak grid scenarios and low-inertia systems are emerging issues in power systems, which could lead to voltage and frequency instabilities. Synchronous Condensers (SynCons) have recently drawn renewed attention as a promising solution to provide system strength and inertia support. Even though the exciter control of SynCons is a well-established technology, further developments are required to guarantee the stability of post-fault operations, in particular, in weak grids. This paper proposes a data-driven approach for designing higher-order optimized exciter controllers to meet this requirement. A pseudo-random binary sequence (PRBS)-based system identification method is used to obtain frequency response data of the power system, from the exciter point of view, which is then fed into the proposed optimal control design procedure. The proposed exciter controller is tested for voltage ride-through and fault scenarios in a single machine infinite bus (SMIB) case and the IEEE 39-bus test system to assess its performance compared to the conventional AC1A exciter controller. The results obtained through simulation tests carried out using the PSCAD/EMTDC software verify that the proposed exciter controller guarantees the post-fault stability in both strong and weak grids.

Control scheme for wind–solar photovoltaic and battery‐based microgrid considering dynamic loads and distorted grid

A concern regarding the deterioration in power quality (PQ) has escalated with the high level of integration of renewable energy sources to the utility, primarily in the scenario of a weak distribution grid. This paper presents a modified complex variable filter (MCVF)-based control to enhance the power quality performance of wind–solar photovoltaic (PV) and battery-based microgrid under the weak grid and dynamic load conditions. An MCVF attenuates the harmonics and DC bias infected voltage and current and extracts the fundamental components from distorted current and voltage. The control scheme for the voltage source converter (VSC) is presented to meet the active power demand of load/grid and attenuates harmonics to control the power quality issues at the point of common coupling. To achieve the round, the clock operation of the microgrid, a battery is interfaced to support the local loads under off-grid mode. Therefore, the presented VSC controller supplies the continuous power to the local loads and maintains the total harmonic distortion value of grid current within the PQ Standard IEEE-519-2014. The simulated and test results are presented to validate the VSC controller in different operating conditions.

Behavior of grid forming converters in different grid scenarios - result of a test campaign on a megawatt scale

Behavior of grid forming converters in different grid scenarios - result of a test campaign on a megawatt scale

Optimisation of Biochar Yield from Sorted Wood Wastes as Sustainable Alternatives to Burning to Ash

Abstract Wood wastes are one of the leading contributors of greenhouse gases cum climate change. While the burning of wood wastes results in CO2 emission, decomposition generates methane. In place of these emissions, wood wastes can be converted into biochars which have a lot of novel applications. However, low biochar yield was the common limitation of wood wastes, essentially, due to a mixture of different classifications of trees that pyrolyse at different conditions. In this study, biochar yield from sorted wood wastes was optimised by varying pyrolysis temperature and time. Power consumption, cost, and carbon footprint of the optimised biochar and ash were compared for both hydropower grid and diesel generator scenarios. Optimal pyrolysis yields were 33.6 % (400 °C), 29.4 % (350 °C) and 18.5 % (400 °C) respectively for hardwood sawdust char (HSC), softwood sawdust char (SSC), and mixed sawdust char (MSC) at 2 hours duration for all materials. The CO2 emissions from ash were 2.49 kg and 19.0 kg respectively for grid and diesel power supply. The diesel generator power was 17 times more expensive than grid power for wood wastes pyrolysis. Wood wastes have better biochar yield when charred homogenously; they have a smaller carbon footprint and are cheaper when produced with power from the grid. Wood wastes in place of being burnt can be processed as inexpensive and environment-friendly biochar.

Electrification potential of U.S. industrial boilers and assessment of the GHG emissions impact

Electrification is a key strategy for decarbonizing the industrial sector. Industrial process heating, which still relies heavily on fossil fuel combustion and accounts for the majority of sector wide GHG emissions, is a particularly attractive electrification target. Electrifying industrial boilers represents a cross-cutting opportunity for GHG emissions reductions, given their widespread use in most manufacturing industries. Yet, there are gaps in the understanding of the current population of conventional industrial boilers in the United States that preclude a characterization of boiler electrification's technical potential to reduce fuel consumption and GHG emissions. In this study, we develop an up-to-date dataset of the industrial boiler population in the U.S. and quantify the county-level electricity requirements and net changes in fuel use and GHG emissions under the current electric grid and theoretical future grid scenarios. Our results show an increase of 105 MMmtCO2e and 73 MMmtCO2e in GHG emissions from boiler electrification, with and without the replacement of byproduct fuels, respectively, under the current electric grid, and a reduction of 19 MMmtCO2e and 7 MMmtCO2e in GHG emissions under a future high renewables electric grid. GHG emissions savings are currently possible only in certain regions of the U.S. unless future grids are decarbonized. We also provide discussion that could be useful for policy makers and manufacturing facilities for advancing the electrification of industrial boilers in locations and industries toward fuel savings and GHG emissions reductions.

Maximum exploitation of electric vehicle storage in smart micro grids with stochastic renewable sources

Dramatic growth in adoption of Renewable Energy Sources (RES) and Electric Vehicles (EV) has led to multiple advantages and challenges in Micro grid (MG) scenario. In this article, a novel prioritization based EV control strategy is implemented in an On-Grid MG (OGMG) scenario. The key objective is to minimize energy cost with maximum utilization of EV storage. The proposed strategy is implemented as a two layer Energy Management System (EMS). Initially Multi Objective Optimization Problem (MOOP) is formulated with two objectives in order to obtain Optimal Power usage (OPU). In second layer of control, Smart EV Prioritization (SEP) is developed that aims to maximize EV storage exploitation and battery longevity. SEP is executed with the help of an adoptive neuro-fuzzy inference system (ANFIS). Multiple decision variables are involved in SEP among which EV usage probability (EUP) and EV owner willingness (EOW) plays a vital role. Both EOW and SEP In the result section, comprehensive analysis is presented with different case studies.

Blockchain Technology on Smart Grid, Energy Trading, and Big Data: Security Issues, Challenges, and Recommendations

The smart grid idea was implemented as a modern interpretation of the traditional power grid to find out the most efficient way to combine renewable energy and storage technologies. Throughout this way, big data and the Internet always provide a revolutionary solution for ensuring that electrical energy linked intelligent grid, also known as the energy Internet. The blockchain has some significant features, making it an applicable technology for smart grid standards to solve the security issues and trust challenges. This study will present a rigorous review of blockchain implementations with the cyber security perception and energy data protections in smart grids. As a result, we describe the major security issues of smart grid scenarios that big data and blockchain can solve. Then, we identify a variety of recent blockchain-based research works published in various literature and discuss security concerns on smart grid systems. We also discuss numerous similar practical designs, experiments, and items that have recently been developed. Finally, we go through some of the most important research problems and possible directions for using blockchain to address smart grid security concerns.

Building energy efficiency and load flexibility optimization using phase change materials under futuristic grid scenario

This paper investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters—such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient—to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants’ thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.