Load Forecast Uncertainty Research Articles

On The Computation of The Locally Closest Bifurcation Point Considering Loading Uncertainties and Reactive Power Limits

Voltage stability assessment on a deterministic direction of loading variation may provide misleadingly high values of voltage stability margin due to uncertainties in load forecast. On the other hand, by disregarding loading forecast information and computing the worst case scenario, we may obtain unrealistic and very conservative margin values. This paper proposes a new voltage stability index that takes place between these two approaches. In this proposal, a set of credible loading variation directions is considered and the closest bifurcation point is determined within this set. A fast method for computation of this index is also proposed. The method is able to detect both saddle-node and limit-induced bifurcations. The efficiency of the proposed method is confirmed by means of numerical simulation on different power systems.

Modeling load forecast uncertainty using generative adversarial networks

The integration of distributed energy resources (DER) increase the uncertainty of the load. Probabilistic load forecasting (PLF) is able to model these uncertainties in the form of quantile, interval, or density. However, the uncertainties are usually given individually for every single period which fails to capture the temporal variations across periods. Therefore, this paper proposes a generative adversarial network (GAN)-based scenario generation approach to model both the uncertainties and the variations of the load. Specifically, point forecasting is first conducted and the corresponding residuals are calculated. On this basis, a conditional GAN model is designed and trained. Then, the well-trained GAN model generates residual scenarios that are conditional on the day type, temperatures, and historical loads. Finally, the effectiveness of the uncertainty modeling by the generated scenarios is evaluated from different perspectives. Case studies on open datasets verify the effectiveness and superiority of the proposed method.

Optimal scheduling of cogeneration microgrid with wind energy and energy storage devices

Aiming at the optimal scheduling of cogeneration microgrid, a microgrid structure including wind energy, energy storage device, solar thermal power station, gas turbine and waste heat boiler is proposed. Based on the uncertainty of wind power output and load forecasting, according to beta distribution wind power output and normal distribution load forecasting, the CHP microgrid optimal scheduling model of photothermal power station is established and solved by genetic algorithm. Finally, the minimum operation cost of the system under different confidence levels is discussed. The results show that the minimum operating cost of the system can be significantly reduced by adding wind power generation into the microgrid structure, and the relationship between the confidence level of load forecasting and the minimum operating cost of the system is studied.

Generation Expansion Planning Considering the Rehabilitation of Aging Generating Units

Aging generating units can gradually lose their efficiency, reliability, and economic viability, and violate certified levels of carbon emission in power system operations. Retirement is traditionally considered a viable option for managing aging units within the generation expansion planning (GEP) studies; while, the rehabilitation of such units can also be economically beneficial to power system and even outperform other options considering certain planning and operation criteria. Rehabilitation procedure depends on many physical factors including generating unit types and locations, as well as financial, regulatory, and transmission network constraints. This paper presents an optimization model for GEP considering retirement and rehabilitation options for generating unit planning and operation. The proposed GEP problem uses the Conditional Value-at-Risk (CVaR) as a risk index for considering rehabilitation cost and load forecast uncertainties. In order to evaluate performance of the proposed method, the IEEE 24-bus and 118-bus systems are examined in various operating conditions and the results are analyzed.

Multi-objective generation expansion and retirement planning using chaotic grasshopper optimisation algorithm

ABSTRACTAn efficient, cost-effective multi-objective generation expansion planning (GEP) with Load Forecast (LF) uncertainty is described and evaluated in this article. The aspects that are considered in this multi-objective GEP model are cost, gas emission, energy price risk, fuel consumption and reliability of the generating units. The retirement of existing generating units is included in the projected model as these units reach their maximumLlifetime. To solve the projected generation expansion and retirement planning model, a Chaotic Grasshopper Optimisation Algorithm (CGOA) is proposed and it is applied into 6-, 14- and 24-year planning periods, and numerical result shows that GEP provides a cost-effective solution with less emission.

Dynamic Data Injection Attack Detection of Cyber Physical Power Systems With Uncertainties

Understanding potential behaviors of attackers is of paramount importance for improving the cybersecurity of power systems. However, the attack behaviors in existing studies are often modeled statically on a single snapshot, which neglects the reality of a dynamically time-evolving power system. Accordingly, a dynamic cyber-attack model with local network information is proposed to characterize the typical data injection attack with the integration of potential dynamic behaviors of an attacker. The proposed model collaboratively alters the meter measurement in a stealthy way to illegally contaminate the system state, thus posing severe threats to cyber physical power systems. We then develop a novel anomaly detection countermeasure from the perspective of state estimation to effectively recognize the dynamic injection attack. In this countermeasure, an interval state forecasting method is proposed to approximate the possible largest variation bounds of each state variable based on a worst-case analysis considering the forecasting uncertainties of renewable energy sources, electric loads, and network parameter perturbations. In addition, the kernel quantile regression is introduced and implemented to formulate the uncertainties in renewable energy and electric load forecast as a series of confidence intervals. When any state variable falls outside its preforecasted intervals, the proposed countermeasure detects the anomaly and sets an alarm condition indicating the possibility of data contamination. Finally, the results from our extensive studies on several IEEE standard test systems have been presented to demonstrate the feasibility of the dynamic attack and the effectiveness of the detection countermeasure.

A priority list-based fuzzy logic controller for short-term unit commitment problem

This paper presents a fuzzy logic-based priority list method to solve the unit commitment problem. The priority list order is selected according to the maximum output power rating of the generating units while satisfying all the constraints over a period of time. The turn on (commitment) and turn off (de-commitment) decision of thermal generating units is handled by priority list order with fuzzy logic algorithm. A fuzzy optimisation-based approach is developed to find the optimal solution by using fuzzy operations and if-then rules. The objective of the optimisation is to achieve minimum operating cost by scheduling of generator units. In solving the unit commitment problem, reliability and feasibility constraints are checked for the planning interval. This method is implemented on standard IEEE 69-bus, 11-thermal generator system. Use of the fuzzy logic controller with priority list method enables the handling of uncertainties in load forecast in an efficient manner.

A priority list-based fuzzy logic controller for short-term unit commitment problem

This paper presents a fuzzy logic-based priority list method to solve the unit commitment problem. The priority list order is selected according to the maximum output power rating of the generating units while satisfying all the constraints over a period of time. The turn on (commitment) and turn off (de-commitment) decision of thermal generating units is handled by priority list order with fuzzy logic algorithm. A fuzzy optimisation-based approach is developed to find the optimal solution by using fuzzy operations and if-then rules. The objective of the optimisation is to achieve minimum operating cost by scheduling of generator units. In solving the unit commitment problem, reliability and feasibility constraints are checked for the planning interval. This method is implemented on standard IEEE 69-bus, 11-thermal generator system. Use of the fuzzy logic controller with priority list method enables the handling of uncertainties in load forecast in an efficient manner.

Impact of Demand-Side Management on the Reliability of Generation Systems

The load shifting strategy is a form of demand side management program suitable for increasing the reliability of power supply in an electrical network. It functions by clipping the load demand that is above an operator-defined level, at which time is known as peak period, and replaces it at off-peak periods. The load shifting strategy is conventionally performed using the preventive load shifting (PLS) program. In this paper, the corrective load shifting (CLS) program is proven as the better alternative. PLS is implemented when power systems experience contingencies that jeopardise the reliability of the power supply, whereas CLS is implemented only when the inadequacy of the power supply is encountered. The disadvantages of the PLS approach are twofold. First, the clipped energy cannot be totally recovered when it is more than the unused capacity of the off-peak period. The unused capacity is the maximum amount of extra load that can be filled before exceeding the operator-defined level. Second, the PLS approach performs load curtailment without discrimination. This means that load clipping is performed as long as the load is above the operator-defined level even if the power supply is adequate. The CLS program has none of these disadvantages because it is implemented only when there is power supply inadequacy, during which the amount of load clipping is mostly much smaller than the unused capacity of the off-peak period. The performance of the CLS was compared with the PLS by considering chronological load model, duty cycle and the probability of start-up failure for peaking and cycling generators, planned maintenance of the generators and load forecast uncertainty. A newly proposed expected-energy-not-recovered (EENR) index and the well-known expected-energy-not-supplied (EENS) were used to evaluate the performance of proposed CLS. Due to the chronological factor and huge combinations of power system states, the sequential Monte Carlo was employed in this study. The results from this paper show that the proposed CLS yields lower EENS and EENR than PLS and is, therefore, a more robust strategy to be implemented.

Generation Adequacy Evaluation Incorporating an Aggregated Probabilistic Model of Active Distribution Network Components and Features

This paper presents a generic methodology for assessing the generation adequacy of power systems incorporating an aggregated probabilistic model representing several active distribution network (ADN) components and features in the form of a virtual power plant (VPP). Randomly varying hourly outputs of individual ADN components like distributed energy resources and loads are combined with information about important distribution network features like topology and constraints to form the aggregated probabilistic model of the VPP using a linearized network flow-based optimization formulation proposed in this paper. The proposed formulation therefore facilitates the VPP’s modeling as a single equivalent ‘unit’ with respect to the transmission grid. This equivalent model is used together with new indices introduced for quantifying different aspects of the VPP’s performance. Impacts of changes in the total installed VPP generation capacity as well as the load forecast uncertainty on the VPP performance indices and the system reliability indices are also investigated. The overall proposed methodology is implemented on the Roy Billinton Test System and the IEEE 69-bus distribution network using Monte Carlo sequential simulation techniques, and results obtained are discussed in detail.

Multi-time period optimized configuration and scheduling of gas storage in gas-fired power plants

With the rapid increase in the proportion of natural gas for power generation, the operations of the power system are increasingly relevant to the supply of natural gas. To improve the reliability of power generation, some additional gas storage equipment should be set up in gas power plants. This paper considered different constraints, objective functions and gas flow/power flow equations at different time scales and built multi-time period optimized configuration and scheduling of gas storage models for gas power plants. Case studies demonstrated the following: (1) The most economical gas storage capacity could be obtained by the first optimal configuration model considering the long-term seasonal fluctuations of gas loads and gas prices. (2) The second model can not only obtain the minimum gas storage configuration capacity to meet the short-term peak load demand but also obtain the day-ahead gas storage schedule. That is, the remaining gas storage capacity at the initial and final points of each day. Moreover, the results can provide some suggestions on the reconstruction of natural gas pipelines. (3) The charge scheme of the gas storage obtained from the third scheduling model considering the uncertainty of the power load forecast can adapt to a certain fluctuating load interval.

Multi-period flexibility forecast for low voltage prosumers

Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

Optimal scheduling of energy storage under forecast uncertainties

Recent studies have concluded that battery energy storage will soon be economically competitive if its cost continues to decline. The authors propose a two-stage look-ahead daily scheduling strategy for distributed energy storage located in distribution networks with a substantial photovoltaic (PV) penetration. They assume that the load serving entity operates this energy storage to harness simultaneously multiple streams of benefits: energy arbitrage, peak shaving, minimising deviations from the load forecast and regulation service. To determine the optimal capacity bid into the day-ahead regulation market and address the price, load, and solar forecast uncertainties, they propose a two-stage optimisation model that bids regulation capacity on the day ahead and determines the storage dispatch schedule in real time. At the day-ahead stage, the load serving entity reserves a portion of the storage capacity for regulation, while the remaining capacity is dispatched for energy arbitrage, peak shaving and minimising the deviations from the forecast. Result suggests that regulation services account for the majority of these benefits. The energy storage is dispatched for peak shaving and forecast-deviation minimisation from around noon to late evening. During the rest of day, storage is dispatched primarily for regulation services.

Evaluasi Keandalan Perencanaan Pembangkit Wilayah Jawa-Bali dengan Mempertimbangkan Ketidakpastian Peramalan Beban

Power system reliability is one of important aspects of power system operation. Lack of capacity in the generation system will affect reliability level of the power system. System evaluation needs to be done to know whether the system is still reliable to supply the load that is projected to increase 8.2% per year. Java-Bali system evaluation planning period 2015-2024 is conducted in this paper. Load forecast uncertainty (LFU) is one of the parameters that needs to be considered when conducting system planning. It is because load tends to be uncertain. This research uses two scenarios: without and with load forecast uncertainty. Reliability indexes LOLE and LOLP are used to evaluate the Java-Bali system. All calculations are based on a program using MATLAB. The result shows that reliability indexes LOLE and LOLP are higher with considering LFU. It means reliability decreases, rather than planning without considering LFU. Moreover, Java-Bali system reliability indexes meet the PLN’s standard value with the best value acquired in 2019.

Residential electrical and thermal storage optimisation in a market environment

This paper presents a method for the energy management of a set of smart homes, in which batteries, thermal storage and demand response are considered as flexibilities in order to achieve minimum operation costs. To cope with the uncertainty of load forecast, a two-stage stochastic optimization process is proposed, in which the first stage decision is the committed energy to be purchased, and the second stage returns the devices’ set-points. This methodology has been developed in the context of the Horizon 2020 project SENSIBLE as part of the tasks related to the use case, Flexibility and Demand Side Management in Market Participation.

Risk-Based Dynamic Security Assessment for Power System Operation and Operational Planning

Assessment of dynamic stability in a modern power system (PS) is becoming a stringent requirement both in operational planning and in on-line operation, due to the increasingly complex dynamics of a PS. Further, growing uncertainties in forecast state and in the response to disturbances suggests the adoption of risk-based approaches in Dynamic Security Assessment (DSA). The present paper describes a probabilistic risk-based DSA, which provides instability risk indicators by combining an innovative probabilistic hazard/vulnerability analysis with the assessment of contingency impacts via time domain simulation. The tool implementing the method can be applied to both current and forecast PS states, the latter characterized in terms of renewable and load forecast uncertainties, providing valuable results for operation and operational planning contexts. Some results from a real PS model are discussed.

Using ensemble weather predictions in district heating operation and load forecasting

Ensemble weather predictions are introduced in the operation of district heating systems to create a heat load forecast with dynamic uncertainties. These provide a new and valuable tool for time-dependent risk assessment related to e.g. security of supply and the energy markets. As such, it is useful in both the production planning and the online operation of a modern district heating system, in particular in light of the low-temperature operation, integration of renewable energy and close interaction with the electricity markets. In this paper, a simple autoregressive forecast model with weather prediction input is used to showcase the new concept. On the study period, its performance is comparable to more complex forecast models. The total uncertainty of the heat load forecast is divided into a constant model uncertainty plus a time-dependent weather-based uncertainty. The latter varies by as much as a factor of 18 depending on the ensemble spread. As a consequence, the total forecast uncertainty varies significantly. The forecast model is applied to the operation of three heat exchanger stations. Applying an optimized temperature control can significantly lower supply temperatures compared to current operation. Improving the temperature control with dynamic time-dependent weather-based uncertainties can lower the supply temperature further and reduce heat losses to the ground. The potential benefit of using dynamic uncertainties is larger for systems with relatively smaller pumping capacities.

Unit Commitment dengan Kekangan Keandalan Menggunakan Algoritme Genetika Mempertimbangkan Ketidakpastian Beban

Unit commitment problem is associated with satisfying load demand and minimizing the total operating cost of the generating unit without violation of unit and system constraints. The solution of unit commitment should be reliable which has adequate spinning reserve in order to anticipate the uncertainty issue. This research proposes a method to handle two uncertainty issues, i.e. unit unavailability and load forecast uncertainty. The unit commitment problem is solved by genetic algorithm with priority list approach. The evaluation of spinning reserve capacity is based on probabilistic criterion by incorporating two reliability indices, i.e. loss of load probability (LOLP) and expected unserved energy (EUE). The effectiveness of the proposed method is tested using IEEE Reliability Test System-1996. The simulation results show that the proposed method is capable for solving unit commitment problem at total operational cost of $ 722186.93 and risk index at 0.347% (while EUE 0.01%, LOLP 1% and standard deviation (SD) 5%). The simulation results also demonstrate that several solution of the method have slightly lower cost up to 0.43% than those obtained in benchmark which the solution based on Simulated Annealing method.

Generating adequacy analysis of Mexico’s national interconnected power system

Purpose Long-term reliability analysis of generation capacity based on the forecasted load demand helps to identify the optimal generation expansion plan of the system. This paper analyzes the generation adequacy of Mexico’s National Interconnected Power System (MNIPS) using loss of load expectation (LOLE) and loss of energy expectation (LOEE) indices. Design/methodology/approach These indices are calculated through an analytical (recursive) method and are then compared with values recommended by the North American Electric Reliability Council (NERC). Weekly indices are computed to analyze the load curtailment options that may occur in some periods. Findings Forecasted values, including load and generation capacity considering maintenance schedules, additions of new generating units and permanently shut down units in accordance with the long-term expanding-system plan have been considered. The load forecast uncertainty is also included. Originality/value This is original work.

Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory

Penetration of smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems. Probability density forecasting methods can effectively quantify the uncertainty of power load forecasting. The paper proposes a short-term power load probability density forecasting method using kernel-based support vector quantile regression (KSVQR) and Copula theory. As the kernel function can influence the prediction performance, three kernel functions are compared in this work to select the best one for the learning target. The paper evaluates the accuracy of the prediction intervals considering two criteria, prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW). Considering uncertainty factors and the correlation of explanatory variables for power load prediction accuracy are of great importance. A probability density forecasting method based on Copula theory is proposed in order to achieve the relational diagram of electrical load and real-time price. The electrical load forecast accuracy of the proposed method is assessed by means of real datasets from Singapore. The simulation results show that the proposed method has great potential for power load forecasting by selecting appropriate kernel function for KSVQR model.