Abstract:With the increase in the permeability of renewable energy and the frequency of extreme weather, the power system requires a large amount of flexible power regulation capacity. In order to realize the multi-day cooperation of reserve resources, the stochastic optimization of medium- and short-term reserve arrangement considering the typhoon uncertainty is studied in this paper. Firstly, the extreme scenario generation and reduction method considering the typhoon path -intensity prediction uncertainty is constructed. Then, considering the combined cost of preventive and emergency control for adequacy in multiple scenarios, the reserve arrangement optimization model in extreme weather is built. In this model, the pre-dispatching strategies for multiple types of reserve resources are proposed to maintain the medium- and short-term coordination. Finally, case studies on a simplified 24-node power system of Zhejiang province, China are presented based on the data of the typhoon Fireworks in July 2021, and the result shows that the proposed reserve arrangement optimization model can reduce the total cost of power systems and the risk of operation under the typhoon disaster.

Abstract:Energy equity refers to the condition in which access to the cleaner energy required by individuals is equally available to all. To relieve the energy expenditures-the key component in the concept of energy equity–of low-income communities, governments worldwide have imposed caps on soaring energy prices. However, the inherent mechanisms within the operational schedule remain undiscussed. This paper innovatively provides guidelines for operators to embed energy burden policies into the bulk power system model, by answering two critical questions. ①What is the impact on system price pattern when embedding the locational price constraints? ② How to reformulate the tie-line schedule to meet the equity thresholds? Consequently, a novel bi-level energy equity-constrained tie-line scheduling model is proposed. The conventional economic dispatch is solved at the upper level, and then a preliminary operational schedule is given to the lower level, where we propose an energy equity slackness component variable to evaluate the gap between preliminary and desired equity-satisfied operational schedules. The implicit constraints on the price are converted into explicit feasibility cuts with dual theory. Case studies on test systems demonstrate the reduced energy expenditure for underserved communities, and the optimal tie-line schedule is also validated.

Abstract:This paper studies the problem of multi-stage robust unit commitment with discrete load shedding. In the day-ahead phase, the on-off status of thermal units is scheduled. During each period of real-time dispatch, the output of thermal units and the action of load shedding are determined, and the discrete choice of load shedding corresponds to the practice of tripping substation outlets. The entire decision-making process is formulated as a multi-stage adaptive robust optimization problem with mixed-integer recourse, whose solution takes three steps. First, we propose and apply partially affine policy, which is optimized ahead of the day and restricts intertemporal dispatch variables as affine functions of previous uncertainty realizations, leaving remaining continuous and binary dispatch variables to be optimized in real time. Second, we demonstrate that the resulting model with partially affine policy can be reformulated as a two-stage robust optimization problem with mixed-integer recourse. Third, we modify the standard nested column-and-constraint generation algorithm to accelerate the inner loops by warm start. The modified algorithm solves the two-stage problem more efficiently. Case studies on the IEEE 118-bus system verify that the proposed partially affine policy outperforms conventional affine policy in terms of optimality and robustness; the modified nested column-and-constraint generation algorithm significantly reduces the total computation time; and the proposed method balances well optimality and efficiency compared with state-of-the-art methods.

Abstract:Maintaining a continuous power balance is crucial for ensuring operational feasibility in power systems. However, due to forecasting difficulties and computational limitations, economic dispatch often relies on discrete interval horizons, which fail to guarantee feasibility within each interval. This paper introduces the concept of a continuous operating envelope for managing intra-interval fluctuations, delineating the range within which fluctuations remain manageable. We propose a parametric programming model to construct the envelope, represented as a polytope that accounts for both timescale and fluctuation dimensions. To address the computational challenges inherent in the parametric programming model, we develop a fast solution method to provide an approximated polytope. The approximated polytope, initially derived from lower-dimensional projections, represents a subset of the exact polytope that ensures operational feasibility. Additionally, we apply a polytope expansion strategy in the original dimensions to refine the approximated polytope, bringing the approximation closer to the exact polytope. Case studies on an illustrative 5-bus and a utility-scale 661-bus system demonstrate that the method effectively and stably provides a continuous operating envelope, particularly for high-dimensional problems.

Abstract:With the load growth and the power grid expansion, the problem of short-circuit current (SCC) exceeding the secure limit in large-scale power grids has become more serious, which poses great challenge to the optimal secure operation. Aiming at the SCC limitations, we use multiple back-to-back voltage source converter based (B2B VSC) systems to separate a large-scale AC power grid into two asynchronous power grids. A multi-objective robust optimal secure operation model of large-scale power grid with multiple B2B VSC systems considering the SCC limitation is established based on the AC power flow equations. The decision variables include the on/off states of synchronous generators, power output， terminal voltage, transmission switching, bus sectionalization, and modulation ratios of B2B VSC systems. The influence of inner current sources of renewable energy generators on the system SCC is also considered. To improve the computational efficiency, a mixed-integer convex programming (MICP) framework based on convex relaxation methods including the inscribed N-sided approximation for the nonlinear SCC limitation constraints is proposed. Moreover, combined with the column-and-constraint generation (C&CG) algorithm, a method to directly solve the compromise optimal solution (COS) of the multi-objective robust optimal secure operation model is proposed. Finally, the effectiveness and computational efficiency of the proposed solution method is demonstrated by an actual 4407-bus provincial power grid and the modified IEEE 39-bus power grid, which can reduce the consumed CPU time of solving the COS by more than 90% and obtain a better COS.

Abstract:With the rapid integration of communication and information technology into substations, the risk of cyber attacks has significantly increased. Attackers may infiltrate substation networks, manipulate switches, and disrupt power lines, potentially causing severe damage to the power system. To minimize such risks, this paper proposes a three-layer defender-attacker-defender (DAD) model for optimally allocating limited defensive resources to substations. To model the uncertainty surrounding the knowledge of defender of potential attacks in real-world scenarios, we employ a fuzzy analytic hierarchy process combined with the decision-making trial and evaluation laboratory (FAHP-DEMATEL). This method accounts for the attack resource uncertainty by utilizing intelligence data on factors potentially influenced by attackers, which serves as an evaluation metric to simulate the likelihood of various attack scenarios. These uncertainty probabilities are then incorporated into the substation DAD model consisting three layers of agents: the decision-maker, the attacker, and the operator. The decision-maker devises a defense strategy before the attack, while the attacker aims to identify the strategy that causes the maximum load loss. Meanwhile, the operator seeks to minimize the load loss through optimal power flow scheduling. To solve the model, the original problem is transformed into a two-layer subproblem and a single-layer master problem, which are solved iteratively using a column-and-constraint generation algorithm. Case studies conducted on the IEEE RTS-96 system and the IEEE 118-node system demonstrate the effectiveness and practicality of the proposed model. Comparative experiments further highlight the advantages of the proposed model.

Abstract:The increasing integration of intermittent renewable energy sources into distribution networks has exerted significant pressure on the frequency regulation of power systems. Meanwhile, integrating small-capacity battery energy storage systems into distribution network is a growing trend in the construction of virtual power plants (VPPs), which offer great potential advantages in improving the system frequency regulation capabilities. However, the process of power dispatch for VPPs may be hindered by imperfections in the communication network, which affects their frequency control performance. Simultaneously, the economic benefits associated with their frequency control services are often overlooked. As such, we propose a co-design method of power dispatch with dynamic power regulation and communication transmission optimization for frequency control in VPPs. First, a joint design scheme of power dispatch and routing optimization under cloud-edge collaborations is proposed. This scheme encompasses a power dispatch method considering the influences of communication network and a routing optimization policy based on graph convolutional neural networks, both of which are designed to ensure the accurate and real-time frequency control service. Further, we propose a dynamic power regulation strategy under edge-edge collaborations. Specifically, according to the established correction control objective, an adaptive distributed auction algorithm (ADAA) based dynamic power regulation control method is designed to determine the optimal regulation power of VPPs, thereby improving the economic benefits of frequency control service. Finally, the simulation results validate the feasibility and superiority of the proposed co-design method for frequency control.

Abstract:Advanced management algorithms are required in modern power systems to sustain energy supply with the highest availability and lowest cost. These algorithms need to be capable of not only maintaining scalability, tractability, and privacy, but also enabling the utilization of grid-edge aggregated flexibilities in transmission systems. This paper proposes a distributed hierarchical transactive energy management (TEM) scheme to manage peak load and line congestion problems using connected and aggregated flexibilities. In the scheme, resource owners can privately solve their respective preference problems and send their scheduled power to the corresponding node operator (NO). Afterward, NOs solve a coordination problem to harmonize the actions of resource owners at the same node. Meanwhile, the independent system operator (ISO) updates control signals to steer the scheduled power to a feasible and optimal point. To accomplish all these, a hybrid decomposition approach is further proposed based on consensus+exchange alternating direction method of multipliers (CE-ADMM) and dual decomposition (DD) (CE-ADMM+DD). Besides, a dynamically constrained cutting plane (DC-CP) update algorithm is evolved to control the feasibility condition and minimize sensitivity to initialization. The proposed hybrid decomposition approach is verified and its performance is compared with other reported approaches. Application to various networks verifies its scalability, enhanced accuracy, and convergence speed.

Abstract:In this paper, policy-assisted graph reinforcement learning (PAGRL) is proposed for real-time economic dispatch (RTED). RTED is presented as a sequential decision problem formulated by Markov decision process (MDP). PAGRL employs a graph convolutional network to extract grid operation features containing topological information and then an agent that performs power dispatch is trained through proximal policy optimization. Moreover, the adaptiveness of agent to more hard-to-learn scenarios is enhanced by difficulty sampling, and policy-assisted action post-processing mechanism is designed to reduce search space and improve decision quality, which provides a general performance enhancement scheme for reinforcement learning in power system applications. Comparative studies on modified IEEE 118-bus system and real-world provincial grid demonstrate the flexible and reliable performance of the proposed PAGRL for RTED.

Abstract:Future power systems will be characterized by low levels of inertia and limited frequency regulation capacity due to the widespread use of renewable energy sources. Furthermore, the response of different types of loads to system disturbances significantly affects the frequency dynamics. To address these issues, this paper proposes an aggregated system frequency response model considering load dynamics. Initially, a dynamic model of different loads is established, followed by the derivation of a small-signal load model that affects the active power imbalance of the system. The active power variations of loads are categorized into three components: load voltage dynamics, load frequency dynamics, and load inertia contribution. These components are incorporated into the system frequency response model, which accounts for load active power dynamics. The final output is an aggregated reduced-order system frequency response model, where the aggregation is primarily weighted by the primary frequency regulation capability of the load, load capacity, and rotor kinetic energy. Finally, the accuracy and effectiveness of the proposed model are validated using the WECC 9-bus test system with power electronic sources, and the influence of load parameters on the frequency stability indicators is analyzed.

Abstract:As the share of renewable generations (RGs) in power systems grows, the demand for peak regulation has increased, leading to higher associated costs. In this paper, we propose a mechanism for allocating peak regulation cost among RGs and distributing compensation among peak regulation resources (PRRs). This mechanism is integrated into a coordinated generation scheduling model to enhance the economic efficiency of independent system operators (ISOs) and incentivize PRRs. First, we propose a model for peak regulation cost of diverse PRRs. Next, we develop a method for constructing an RG output curve that facilitates peak regulation. The waveform difference between this constructed curve and the RG forecasted output curve is then calculated. In addition, we create a mechanism for peak regulation cost allocation and compensation distribution that incorporates the waveform difference, the peak regulation contribution of PRRs, and participant satisfaction as key indicators. We then establish a coordinated generation scheduling model using this mechanism, which is solved through the column-and-cut generation algorithm and rolling optimization. Finally, we conduct case studies based on an improved IEEE 30-bus test system and perform several comparative analyses to validate the effectiveness of the proposed mechanism and coordinated generation scheduling model.

Abstract:To maintain the frequency stability of power systems integrated with large-scale renewable energy sources (RESs), a frequency-constrained unit commitment (FCUC) model is proposed, which incorporates a coordinated frequency control strategy of wind turbines and energy storage system (WT-ESS), a vital component for enhancing frequency regulation capacity of wind farms. Analytical formulations for the maximum rate of change of frequency (RoCoF) and steady-state frequency deviation are derived for both serial control and parallel control, accounting for the output-limited state of energy storage under serial control. To address the problem of solution slowness caused by the strong nonlinear frequency nadir constraints, a model-based multi-directional bilayer solution method is proposed. This method employs the simulation model to detect whether the frequency nadir constraint is active and generates parallel optimized cuts in three directions. Simulation results on the IEEE 39-bus test system demonstrate that the proposed FCUC model and solution method could accurately reflect the primary frequency regulation (PFR) characteristics of WT-ESS. Furthermore, the coordinated frequency control strategy effectively reduces overall operating costs while ensuring frequency security.

Abstract:This work presents a new topological observability algorithm to strengthen the performance of the hybrid power system static state estimation, assuming that the supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements are recorded at the same time intervals. The observability of each estimated state variable is assessed by the value assigned to its least local redundancy index. The algorithm has been specifically developed to enhance the observability of an existing wide-area monitored system and exempts its expansion from critical sets and critical measurements. These objectives are achieved by building the incidence matrices of the measurements for the nodes and branches. The performance of the proposed algorithm is evaluated using the IEEE test systems and the SIN test systems of Brazilian equivalent systems.

Abstract:The high share of intermittent wind power jeopardizes system frequency security in isolated offshore field microgrids (IOFMs). Existing scheduling strategies, mainly focusing on stable energy supply and demand, fail to ensure frequency security due to the limited flexible and dispatchable resources in the IOFM. Thus, this paper proposes an optimal scheduling model of wind power generators with unified frequency response and spinning reserve constraints to assist operators in efficiently managing turbine generators. Frequency security indices are introduced to quantify the impact of both sudden wind power shortages and continuous wind power fluctuations on the frequency dynamics under different control modes. Based on these indices, unified frequency response and spinning reserve constraints are analytically derived to support the optimization of the control mode and on/off status of wind power generators. These highly nonlinear unified constraints are then reformulated as mixed-integer linear constraints, which are integrated into the scheduling model with operating costs as the objective. The proposed model is tested using a modified real-world IOFM. The results demonstrate that the proposed model not only ensures system frequency security but also reduces operating costs and carbon emissions.

Abstract:Interval state estimation (ISE) can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements, thereby analyzing the impact of uncertain pseudo-measurements on states. However, predicted pseudo-measurements have prediction errors, and their confidence intervals do not necessarily contain the truth values, leading to estimation biases of the ISE. To solve this problem, this paper proposes a pseudo-measurement interval prediction framework based on the Gaussian process regression (GPR) model, thereby improving the prediction accuracy of pseudo-measurement confidence intervals. Besides, a weight assignment strategy for improving the robustness of weighted least squares (WLS) ISE is proposed. This strategy quantifies the deviation between the pseudo-measurement intervals and their estimated intervals and assigns smaller weights to the pseudo-measurement intervals with larger deviations, thereby improving the estimation accuracy and robustness of the ISE. This paper adopts the data from the supervisory control and data acquisition (SCADA) system of the New York Independent System Operator (NYISO). It verifies the advantages of the GPR method for pseudo-measurement interval prediction by comparing it with the quantile regression and neural network methods. In addition, this paper demonstrates the effectiveness of the proposed weight assignment strategy through the IEEE 14-bus case. Finally, the differences in the estimation accuracy and the bad data identification between the robust interval state estimation and deterministic state estimation are discussed.

Abstract:As the proportion of renewable energy (RE) increases, the inertia and the primary frequency regulation (FR) capability of the power system decrease. Thus, ensuring frequency security in the scheduling model has become a new technical requirement in power systems with a high share of RE. Due to a shortage of conventional synchronous generators, the frequency support of multi-source converters has become an indispensable part of the system frequency resources, especially variable-speed wind turbine generation (WTG) and battery energy storage (BES). Quantitative expression of the FR capability of multi-source converters is necessary to construct frequency-constrained scheduling model. However, the frequency support performance of these converter-interfaced devices is related to their working states, operation modes, and parameters, and the complex coupling of these factors has not been fully exploited in existing models. In this study, we propose an integrated frequency-constrained scheduling model considering the coordination of FR capabilities from multi-source converters. Switchable FR control strategies and variable FR parameters for WTG with or without reserved power are modeled, and multi-target allocation of BES capacity between tracking dispatch instruction and emergency FR is analyzed. Then, the variable FR capabilities of WTG and BES are embedded into the integrated frequency-constrained scheduling model. The nonlinear constraints for frequency security are precisely linearized through an improved iteration-based strategy. The effectiveness of the proposed model is verified in a modified IEEE 24-bus standard system. The results suggest that the coordinated participation of BES and WTG in FR can effectively reduce the cost of the scheduling model while meeting frequency security constraints.

Abstract:We consider a power system whose electric demand pertaining to freshwater production is high (high freshwater electric demand), as in the Middle East, and investigate the tradeoff of storing freshwater in tanks versus storing electricity in batteries at the day-ahead operation stage. Both storing freshwater and storing electricity increase the actual electric demand at valley hours and decrease it at peak hours, which is generally beneficial in term of cost and reliability. But, to what extent? We analyze this question considering three power systems with different generation-mix configurations, i.e., a thermal-dominated mix, a renewable-dominated one, and a fully renewable one. These generation-mix configurations are inspired by how power systems may evolve in different countries in the Middle East. Renewable production uncertainty is compactly modeled using chance constraints. We draw conclusions on how both storage facilities (freshwater and electricity) complement each other to render an optimal operation of the power system.

Abstract:Base station (BS) backup batteries (BSBBs), with their dispatchable capacity, are potential demand-side resources for future power systems. To enhance the power supply reliability and post-contingency frequency security of power systems, we propose a two-stage stochastic unit commitment (UC) model incorporating operational reserve and post-contingency frequency support provisions from massive BSBBs in cellular networks, in which the minimum backup energy demand is considered to ensure BS power supply reliability. The energy, operational reserve, and frequency support ancillary services are co-optimized to handle the power balance and post-contingency frequency security in both forecasted and stochastic variable renewable energy (VRE) scenarios. Furthermore, we propose a dedicated and scalable distributed optimization framework to enable autonomous optimizations for both dispatching center (DC) and BSBBs. The BS model parameters are stored and processed locally, while only the values of BS decision variables are required to upload to DC under the proposed distributed optimization framework, which safeguards BS privacy effectively. Case studies on a modified IEEE 14-bus system demonstrate the effectiveness of the proposed method in promoting VRE accommodation, ensuring post-contingency frequency security, enhancing operational economics, and fully utilizing BSBBs ’ energy and power capacity. Besides, the proposed distributed optimization framework has been validated to converge to a feasible solution with near-optimal performance within limited iterations. Additionally, numerical results on the Guangdong 500 kV provincial power system in China verify the scalability and practicality of the proposed distributed optimization framework.

Abstract:Volt-var control (VVC) is essentially a non-convex optimization problem due to the non-convexity of power flow (PF) constraints, resulting in the difficulty in obtaining the optimum without convexity conversion. The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables, which in turn increases the optimization difficulty or even leads to optimization failure. This paper first proposes a deterministic VVC method based on convex deep learning power flow (DLPF). This method uses the input convex neural network (ICNN) to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment, which can ensure the global optimum with extremely fast computation speed. To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC, this paper proposes robust VVC method based on convex deep learning interval power flow (DLIPF), which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval. Combining DLIPF with DLPF, this method decreases the modeling and optimization difficulty of robust VVC significantly. Test results on 30-bus, 118-bus, and 200-bus systems prove the correctness and rapidity of the proposed methods.

Abstract:The increasing penetration of renewable energy sources (RESs) brings great challenges to the frequency security of power systems. The traditional frequency-constrained unit commitment (FCUC) analyzes frequency by simplifying the average system frequency and ignoring numerous induction machines (IMs) in load, which may underestimate the risk and increase the operational cost. In this paper, we consider a multi-area frequency response (MAFR) model to capture the frequency dynamics in the unit scheduling problem, in which regional frequency security and the inertia of IM load are modeled with high-dimension differential algebraic equations. A multi-area FCUC (MFCUC) is formulated as mixed-integer nonlinear programming (MINLP) on the basis of the MAFR model. Then, we develop a multi-direction decomposition algorithm to solve the MFCUC efficiently. The original MINLP is decomposed into a master problem and subproblems. The subproblems check the nonlinear frequency dynamics and generate linear optimization cuts for the master problem to improve the frequency security in its optimal solution. Case studies on the modified IEEE 39-bus system and IEEE 118-bus system show a great reduction in operational costs. Moreover, simulation results verify the ability of the proposed MAFR model to reflect regional frequency security and the available inertia of IMs in unit scheduling.

Abstract:With the large-scale integration of renewable energy, the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy generation. Simultaneously, in a market-oriented operation mode, the power dispatching control center aims to reduce the overall power purchase cost while ensuring the security of the power system. Therefore, a security-constrained transmission maintenance optimization model considering generation and operational risk costs is proposed herein. This model is built on double-layer optimization framework, where the upper-layer model is used for maintenance and generation planning, and the lower-layer model is primarily used to address the operational security risk arising from the random prediction error and N -1 transmission failure. Correspondingly, a generation-maintenance iterative algorithm based on a defined cost feedback is included to increase solution efficiency. Generation cost is determined using long-term security-constrained unit commitment, and the operational risk cost is obtained using a double-layer N-1 risk assessment model. An electrical correlation coupling coefficient is proposed for the solution process to avoid maintenance of associated equipment simultaneously, thereby improving model convergence efficiency. The IEEE 118-bus system is used as a test case for illustration, and test results suggest that the proposed model and algorithm can reduce the total cost of transmission maintenance and system operation while effectively improving the solution efficiency of the joint optimization model.

Abstract:Previous studies have demonstrated that disharmony among voltage-source-controlled units (VSCUs) may occur on an alternating current (AC) transmission or distribution line under steady-state operating conditions (SSOCs) or quasi-static operating conditions (QSSOCs). As the studies on frequency disharmony have been expanded to multiple disharmonized VSCUs in the local power grid, its adverse effects on AC lines and equivalent load (EL) at the bus without active voltage control ability (non-active bus) need to be investigated further. Considering the locality of disharmony and common topological connections among VSCUs, this paper adopts a Y-type three-terminal local power grid (LPG) as the research object. The disharmony among the three VSCUs is discussed. Firstly, for the load at non-active bus, the formulas for single-phase instantaneous voltage, load current, load power, as well as average power under disharmony operating conditions (DOCs) are derived. The characteristic indicators of the above electrical quantities are defined, which can measure the amplification and reduction degrees of the above electrical quantities before and after disharmony. Secondly, for the line directly connected to VSCUs, the formulas for single-phase instantaneous line current and power and the average power under DOCs are derived. The characteristic indicators of power flow are defined, which can be used to quantify the peak amplification impact of oscillation before and after disharmony. Finally, the case study on the Y-type three-terminal LPG under the single-disharmony and the multi-disharmony switching scenarios indicates that the long-period power oscillation caused by disharmony may occur in the load flow at the non-active bus and the line flow. The oscillation causes a serious decrease in load capability and a significant amplification of the peak of line power oscillation.

Abstract:The optimal planning and operation of multi-type flexible resources (FRs) are critical prerequisites for maintaining power and energy balance in regional power grids with a high proportion of clean energy. However, insufficient consideration of the multi-dimensional and heterogeneous features of FRs, such as the regulation characteristics of diversified battery energy storage systems (BESSs), poses a challenge in economically relieving imbalance power and adequately sharing feature information between power supply and demand. In view of this disadvantage, an optimal planning and operation method based on differentiated feature matching through response capability characterization and difference quantification of FRs is proposed in this paper. In the planning stage, a model for the optimal planning of diversified energy storages (ESs) including Lithium-ion battery (Li-B), supercapacitor energy storage (SCES), compressed air energy storage (CAES), and pumped hydroelectric storage (PHS) is established. Subsequently, in the operating stage, the potential, direction, and cost of FR response behaviors are refined to match with the power and energy balance demand (PEBD) of power grid operation. An optimal operating algorithm is then employed to quantify the feature differences and output response sequences of multi-type FRs. The performance and effectiveness of the proposed method are demonstrated through comparative studies conducted on an actual regional power grid in northwest China. Analysis and simulation results illustrate that the proposed method can effectively highlight the advantages of BESSs compared with other ESs, and economically reduce imbalance power of the regional power grid under practical operating conditions.

Abstract:The integration of photovoltaic power generation is a new development into the traction power supply system (TPSS). However, traditional research on the TPSS operation strategy has not fully considered the risk of uncertainty in photovoltaic power output. To this end, we propose an operation strategy for the rail transit green energy system that considers the uncertainty risk of photovoltaic power output. First, we establish a regenerative braking energy utilization model that considers the impact of time-of-use (TOU) electricity price on the utilization efficiency and economic profit of regenerative braking energy and compensates for non-traction load. Then, we propose an operation strategy based on the balance of power supply and demand that uses an improved light robust (ILR) model to minimize the total cost of the rail transit green energy system, considering the risk of uncertainty in photovoltaic power output. The model incorporates the two-step load check on the second-level time scale to correct the operational results, solve the issue of different time resolutions between photovoltaic power and traction load, and achieve the coordinated optimization of risk cost and operation cost after photovoltaic integration. Case studies demonstrate that the proposed model can effectively consider the impact of the uncertainty in photovoltaic power output on the operation strategy, significantly improving the efficiency and economy of the system operation.

Abstract:Nowadays, public policies in Chile are geared towards the promotion of distributed energy resources (DERs) such as distributed photovoltaic (PV) systems. However, the prevailing socioeconomic context and the lack of incentive to invest in DERs have posed a challenge to achieving the established goals in the coming years. This paper develops a three-entity architecture model and decision-making algorithms for peer-to-peer (P2P) PV energy trading. It seeks to conduct a sensitivity analysis of a P2P PV energy trading system in a community microgrid, to assess the potential benefits for local communities and to encourage the development of new local public policies aimed at enhancing the profitability of DERs. Various scenarios are compared, both with and without P2P market, considering residential customers (RCs), encompassing both consumers and prosumers with PV systems, with or without battery energy storage systems (BESSs), an aggregator (AG), and utility grid (UG). Daily energy and economic transactions are examined with the aim of reducing the annual electricity bills for each RC, enhancing the profitability of DERs for prosumers, increasing incomes for the AG, and exploring potential benefits for the UG. The load profiles and meteorological data are collected from publicly available databases, and a novel electricity pricing scheme is proposed based on current rates offered by the local UG. The results demonstrate that the P2P market could lead to a reduction in the annual electricity bills by as much as 1.76% for consumers, an increase in annual income of up to 149% for prosumers, and a reduction in the payback period for their DERs by up to 0.4 years. This paper contributes to improving the investment in DER projects and provides a guide for extending the work to different regions of Chile and global emerging economies with DER potential.