Hasil untuk "Distribution or transmission of electric power"

Warren Farmer, Ulrich Minnaar

Marvin Dorn, Jonas Lotze, Uwe Kuehnapfel et al.

Due to the energy transition, which involves phasing out base load power plants such as coal, there is a need to establish storage systems within the energy system to compensate for fluctuations of renewable energies. Batteries are suitable for day-night cycles and particularly for short-cycle applications. To address the problem of dark-doldrums, when neither wind nor solar energy is available, gas and, in the more distant future, hydrogen power plants are to be used. By combining batteries and hydrogen power plants in a hybrid energy storage system, further advantages and application possibilities arise regarding grid stability and system design. This work illustrates interrelationships between the subsystems, optimizes proportions, and demonstrates logical system sizes, technologies, and their costs. A central part of the work are the self-derived methods for system design and the justification of these. Storage pressure, running times, availability time, annual cycles and design of the subsystems are described. Systems of this scale are difficult to imagine. A program developed as part of this work to implement the methods, visualizes the system, displays the system parameters, and shows the best-case and worst-case capital expenditures. An optimized system design is presented. Different combinations in the system design show the effects on capital expenditures. Starting from 2 to 4 hours of availability time, the hybrid system becomes cheaper than a pure battery system in terms of capital expenditures.

Michael Eichelbeck, Matthias Althoff

Energy communities (ECs) are an increasingly studied path toward improving prosumer coordination. A central challenge of ECs is to allocate cost savings fairly to members. While many allocation mechanisms have been developed, existing literature does not account for the implications of inaccurate forecasts on the fairness of the allocation. We introduce a set of fairness conditions for imperfect knowledge allocation and show that these conditions constitute a Pareto front. We demonstrate how a well-established allocation scheme, the Shapley value mechanism (SVM), has unfavorable consequences for flexibility-providing community members and generally does not yield solutions on this Pareto front. In contrast, we interpret dispatch cost under imperfect knowledge as being composed of two components. The first represents the cost under perfect knowledge, and the second represents the cost arising from inaccurate forecasts. Our proposed mechanism extends an SVM-based allocation of the perfect knowledge cost by allocating the remaining cost in a way that guarantees finding solutions on the Pareto front. To this end, we formulate a convex multi-objective optimization problem that can efficiently be solved as a linear or quadratic program.

Sampson E. Nwachukwu, Komla A. Folly, Kehinde O. Awodele

This paper presents a soft actor-critic (SAC)-based method for solving the solar photovoltaic (PV) Maximum Power Point Tracking (MPPT) control problem under partial shading conditions (PSCs). The MPPT method optimizes the solar PV power and ensures that it constantly operates at its “maximum power point (MPP),” regardless of the dynamics of weather conditions. Traditional MPPT methods, such as the perturb and observe (P&O) method, are commonly employed to solve the MPPT control problem. However, they often suffer from a slower convergence rate, significant oscillation near the MPP, drift problems. Additionally, in the presence of partial shading, they frequently fail to track the solar PV global maximum power point (GMPP). These problems were addressed using the deep Q-network (DQN) method. However, DQN cannot be applied to continuous action spaces. It also uses inefficient experience replay and suffers from Q-value overestimation. Thus, under PSCs and certain environmental conditions, DQN produces fluctuations of power close to the MPP or GMPP, resulting in power loss. To solve the MPPT control task, mathematical models of the Markov Decision Process, solar PV system, and boost converter were developed. Key hyperparameters affecting the SAC algorithm’s performance were also investigated. Furthermore, the P&O method was developed for comparison. Simulation results show that the SAC-based MPPT method achieved better tracking accuracy than the DQN method under standard testing conditions, varying irradiance levels, and PSCs. Also, it is shown that both the DQN and SAC methods have superior tracking performance compared to the P&O method under similar environmental conditions tested.

Jagannath Wijekoon, Athula D. Rajapakse

Yang Liu, Chenyang Yang, Nanpeng Yu et al.

Abstract The active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed.

Rebecca O'Neil, Konstantinos Oikonomou, Vince Tidwell et al.

Energy and water systems are deeply interdependent yet organized and managed into separate sectors. Although technological innovations emerge at the intersection of energy and water, these sectors largely operate independently, despite their mutual importance. This persistent challenge is structural, as the sectors are organized and managed as separate systems. More can be done to integrate these sectors for mutual benefit and resilience. This paper provides an overview and a useful categorization of six research areas that bridge the water and energy sectors: integrated planning, integrated operations, data and analytics, policy and economics, hydropower and marine energy, and resilience. The authors lead the IEEE Power & Energy Society Task Force on Water-Power Systems (WPS), which represents an international and rapidly growing collaboration across both energy and water sectors to find common areas of cooperation and innovation. Through the collective efforts of this Task Force, a comprehensive roadmap on water power systems integration was issued in 2023. The paper presents evidence that coordinated efforts in data analytics, policy, and economic interventions can significantly advance hydropower, marine energy, and energy storage technologies, ultimately enhancing the resilience and efficiency of both water and power infrastructures.

Nitasha Sahani, Chen-Ching Liu

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

Lang Li, Xinyu An, Ke Zhou et al.

Abstract For the problem that traditional droop control cannot maintain the maximum output power of photovoltaic (PV) units, this work proposes a self‐adaptive communication‐free control scheme for the islanded PV‐storage AC microgrids. The proposed control enables the maximum power point tracking‐based output active powers of PVs by adaptively adjusting P‐f/Q‐V droop coefficients. It also facilitates adaptive allocations of reactive powers based on the available capacities of PVs and storage modules. The key characteristics of the proposed control strategy are summarized as follows: 1) PV units are controlled as voltage sources, which could participate in the voltage/frequency regulation to a certain extent; 2) maximum power utilization of PVs is obtained; 3) adaptive allocations of reactive powers are realized based on the maximum available capacity of PVs and storage modules. Subsequently, the stability analysis of the proposed self‐adaptive communication‐free control strategy is verified. Finally, the validity of the proposed self‐adaptive communication‐free control method is validated through simulations conducted using MATLAB/Simulink.

Saira Manzoor, Mairaj‐ud‐din Mufti, Farhad Ilahi Bakhsh et al.

Abstract Traditionally, phase shifters powered by power electronics are used in conjunction with energy storage devices to improve the load frequency control. Conventional phase shifters provide minimal inertia and offer a significant threat to power quality due to harmonics. This study suggests the type‐2 fuzzy based adaptively predictive controlled variable frequency transformer (F2‐APC‐VFT) as a phase shifter to work in coordination with superconducting magnetic energy storage (SMES) for improved load frequency control. To assess the effectiveness of coordinated control, a two‐area, four‐machine linked power system with F2‐APC‐VFT installed in succession with the connecting line near to area one and SMES in the other area is employed. A type‐2 fuzzy controller is used to tune the cost function weights of the adaptive predictive controller (APC). A simulation study is conducted in MATLAB to show the effectiveness of the suggested strategy. Tuning the cost function weights with a type‐2 fuzzy controller considerably impacts in minimising the frequency and tie‐power deviations. The effectiveness of the F2‐APC is verified by comparing its performance with that of an adaptive neuro fuzzy inference system (ANFIS) controller and constant weight‐based adaptive predictive controller (C‐APC).

Boyu Cheng, Linda Nozick, Ian Dobson et al.

This paper develops a probabilistic earthquake risk assessment for the electric power transmission system in the City of Los Angeles. Via a dc load flow analysis of a suite of damage scenarios that reflect the seismic risk in Los Angeles, we develop a probabilistic representation for load shed during the restoration process. This suite of damage scenarios and their associated annual probabilities of occurrence are developed from 351 risk-adjusted earthquake scenarios using ground motion that collectively represent the seismic risk in Los Angeles at the census tract level. For each of these 351 earthquake scenarios, 12 damage scenarios are developed that form a probabilistic representation of the consequences of the earthquake scenario on the components of the transmission system. This analysis reveals that substation damage is the key driver of load shed. Damage to generators has a substantial but still secondary impact, and damage to transmission lines has significantly less impact. We identify the census tracts that are substantially more vulnerable to power transmission outages during the restoration process. Further, we explore the impact of forecasted increases in penetration of residential storage paired with rooftop solar. The deployment of storage paired with rooftop solar is represented at the census tract level and is assumed to be able to generate and store power for residential demand during the restoration process. The deployment of storage paired with rooftop solar reduces the load shed during the restoration process, but the distribution of this benefit is correlated with household income and whether the dwelling is owned or rented.

Amirhossein Sajadi, Bri-Mathias Hodge

Contemporary theories and models for electric power system stability are predicated on a widely held assumption that the mechanical inertia of the rotating mass of synchronous generators provides the sole contribution to stable and synchronized operation of this class of complex networks on subsecond timescales. Here we formulate the electromagnetic momentum of the field around the transmission lines that transports energy and present evidence from a real-world bulk power network that demonstrates its physical significance. We show the classical stability model for power networks that overlooks this property, known as the "swing equation", may become inadequate to analyze systems with high shares of inverter-based resources, commonly known as "low-inertia power systems". Subsequently, we introduce a plane wave dynamic model, consistent with the structural properties of emerging power systems with up to 100% inverter-based resources, which identifies the concept of inertia in power grids as a time-varying component. We leverage our theory to discuss a number of open questions in the electric power industry. Most notably, we postulate that the changing nature of power networks with a preponderance of variable renewable energy power plants could strengthen power network stability in the future; a vision which is irreconcilable with the conventional theories.

Niloofar Pourghaderi, Mahmud Fotuhi‐Firuzabad, Moein Moeini‐Aghtaie et al.

Abstract This paper presents a two‐stage adaptive robust optimization framework for day‐ahead energy and intra‐day flexibility self‐scheduling of a technical virtual power plant (TVPP). The TVPP exploits diverse distributed energy resources’ (DERs) flexibility capabilities in order to offer flexibility services to wholesale flexibility market as well as preserving the distribution network's operational constraints in the presence of DER uncertainties. The TVPP aims at maximizing its profit in energy and flexibility markets considering the worst‐case uncertainty realization. In the proposed framework, the first stage models the TVPP's participation strategy in day‐ahead energy market and determines the DERs’ optimal energy dispatch. The second stage addresses the TVPP's strategy in intra‐day flexibility market to determine the DERs’ optimal flexibility capability provision by adjusting their energy dispatch for the worst‐case realization of uncertainties. The uncertainty characteristics associated with photovoltaic units, electric vehicles, heating, ventilation and air conditioning systems, and other responsive loads as well as the transmission network's flexibility capability requests are considered using an adaptive robust approach. Adopting the duality theory, the model is formulated as a mixed‐integer linear programming problem and is solved using a column‐and‐constraint generation algorithm. This model is implemented on a standard test system and the model effectiveness is demonstrated.

Zhou Chao, Sun Jianfeng

Abstract In early spring or winter, supercooled raindrops impact on transmission lines and accumulate ice on its surface frequently, which may induce galloping vibration occurs. Studies on collision coefficient or icing mostly focus on key parameters of wind velocities, raindrop sizes and conductor diameters, whereas few involve electric field. In order to clarify the influence of the direct current electric field on the collision coefficient of transmission lines, a calculation formula of the mass flow rate of raindrops was proposed, and the process of raindrops hitting the conductor surface is numerically calculated by the Euler–Lagrange method. The effects of electric field intensity, conductor diameter, cross‐sectional shape, wind velocity, raindrop diameter and charge density on the collision coefficient of transmission lines are analyzed. The results show that the collision coefficient decreases with the increase of electric intensity. As the electric intensity increases from 0  to 66.7 kV/cm, the collision coefficient decreases about 33%. Because of the repulsion between the conductor and raindrops, more raindrops move away from the conductor surface and collision coefficient decreases. The collision coefficient of the actual conductor under no electric field is slightly smaller than that of the simplified circular cross‐section. Under DC electric field, the collision coefficient of the conductor decreases with wind velocity increase up to 8 m/s. Beyond that threshold, the collision coefficient is almost constant.

Javier Renedo, Luis Rouco, Aurelio Garcia‐Cerrada et al.

Abstract Multi‐terminal high‐voltage direct current technology based on voltage‐source converter stations (VSC‐MTDC) is expected to be one of the most important contributors to the future of electric power systems. In fact, among other features, it has already been shown how this technology can contribute to improve transient stability in power systems by the use of supplementary controllers. Along this line, this paper will investigate in detail how these supplementary controllers affect electromechanical oscillations, by means of small‐signal stability analysis. The paper analyses two control strategies based on the modulation of active‐power injections (P‐WAF) and reactive‐power injections (Q‐WAF) in the VSC stations which were presented in previous work. Both control strategies use global signals of the frequencies of the VSC‐MTDC system and they presented significant improvements on transient stability. The paper will provide guidelines for the design of these type of controllers to improve both large‐ and small‐disturbance angle stability. Small‐signal stability analysis (in Matlab) has been compared with non‐linear time domain simulation (in PSS/E) to confirm the results using CIGRE Nordic32A benchmark test system with a VSC‐MTDC system. The paper analyses the impact of the controller gains and communication latency on electromechanical‐oscillation damping. The main conclusion of the paper is that transient‐stability‐tailored supplementary controllers in VSC‐MTDC systems can be tuned to damp inter‐area oscillations too, maintaining their effectiveness.

Hanko Ipach, Leonard Fisser, Christian Becker et al.

Abstract Low‐Voltage (LV) distribution grids are facing a rapid increase of connected Photovoltaic (PV) power plants as well as flexible consumers like Battery‐Electric Vehicle (BEV) chargers and Heat Pumps (HPs). The coordinated operation of these generation, storage and consumption units, referred to as Distributed Energy Resources (DERs), is regarded as a key requirement to maximize the benefits of renewable generation without violating, for example, voltage limits. Therefore, an operation management scheme was proposed in previous work that optimizes the power flows in LV grids in real‐time, where optimality is expressed as a maximization of the utility that DER owners experience from power consumption or injection, respectively. In this contribution, this method is extended by: (1) detailing a time‐varying utility‐model to express customer needs, (2) introducing a distributed implementation enhancing the robustness to failures, (3) developing a testbed using a real‐time digital power grid simulator and a communication network emulator, and (4) integrating a real‐time information flooding protocol. The performance is evaluated in different simulation scenarios, showing that the proposed method is able to cooperatively utilize the flexible units in order to fulfil the DER owners' needs even in the event of controller failures and constrained communication.

Tong Lu, Sizu Hou, Yan Xu

Abstract When a grounding fault occurs in the distribution network with distributed generation, the network topology becomes intricate, making it challenging to extract fault characteristics, resulting in a decrease in the precision of fault discrimination. To address this issue, a graph isomorphism network (GIN) approach based on the parameterized adaptive graph learning (AdapGL) module is proposed, transforming the distribution network fault selection problem into a graph classification task. First, the adjacency matrix of the distribution network's topology graph is initialized. This matrix, combined with feature vectors from the robust local mean decomposition energy entropy and transient dielectric loss angle of line, will be input into the GIN. Then, the AdapGL module is integrated into the GIN, dynamically learning and updating the one‐way relationships between actual network nodes to complete the graph classification task. Finally, a radial distribution network model (RDNM) and an improved IEEE 34 nodes model are established, and the fault selection results of the AdapGL‐GIN method are compared with those of other methods. The results indicate that the proposed method achieves higher accuracy than other methods, demonstrating significant practical importance in engineering applications.

Yadong Zhang, Pranav M Karve, Sankaran Mahadevan

A DC OPF surrogate modeling framework is developed for Monte Carlo (MC) sampling-based risk quantification in power grid operation. MC simulation necessitates solving a large number of DC OPF problems corresponding to the samples of stochastic grid variables (power demand and renewable generation), which is computationally prohibitive. Computationally inexpensive surrogates of OPF provide an attractive alternative for expedited MC simulation. Graph neural network (GNN) surrogates of DC OPF, which are especially suitable to graph-structured data, are employed in this work. Previously developed DC OPF surrogate models have focused on accurate operational decision-making and not on risk quantification. Here, risk quantification-specific aspects of DC OPF surrogate evaluation is the main focus. To this end, the proposed GNN surrogates are evaluated using realistic joint probability distributions, quantification of their risk estimation accuracy, and investigation of their generalizability. Four synthetic grids (Case118, Case300, Case1354pegase, and Case2848rte) are used for surrogate model performance evaluation. It is shown that the GNN surrogates are sufficiently accurate for predicting the (bus-level, branch-level and system-level) grid state and enable fast as well as accurate operational risk quantification for power grids. The article thus develops tools for fast reliability and risk quantification in real-world power grids using GNN-based surrogates.

Yang Li, Wenjie Ma, Fanjin Bu et al.

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.

Wenlong Chen, Yingang Gui, Tao Li et al.

Abstract Ring main unit acts as an important role in modern power transmission and distribution systems. Partial electric discharge frequently occurs in ring main unit because of insulation faults. Along with the partial electric discharge, the air filled in ring main unit decomposes to various decomposition products, where NO, CO, O3, N2O4 are the characteristic products. This work devotes to detect the partial electric discharge by detecting these four characteristic gases. Based on first-principle calculation, Pd-modified gallium nitride nanotubes (Pd-GaNNTs) was proposed as a novel gas sensor for these gases detection. Pd atom doping greatly enhances the surface activity of GaNNTs. Gas-sensing properties and mechanism of Pd-GaNNTs to these four characteristic gases were analyzed based on adsorption energy, charge transfer amount, total density of states, projected density of states, and the molecular orbits calculations. The adsorption stability of Pd-GaNNTs to these gases ranked as: N2O4 > CO > O3 > NO, and the effect of gas adsorption on the conductivity is: O3 > NO > N2O4 > CO. Calculation results show that Pd-GaNNTs could be a promising material to prepare new generation gas sensor using in partial electric discharge detection in ring main unit.