Short-term Power System Research Articles

Shore Power System Load Forecasting Model based on IDBO algorithm and PCA-BiLSTM Network

Shore power offers convenient and sustainable power services to ships docking in ports, playing a pivotal role in advancing environmental preservation and the effective utilization of resources. Shore power system loads are susceptible to numerous influencing factors. Therefore, it is crucial to identify a highly accurate load forecasting model to facilitate the judicious allocation of power resources. A short-term shore power system load forecasting model based on the improved dung beetle optimization algorithm (IDBO) and principal component analysis (PCA) in conjunction with the enhanced bidirectional long and short-term memory (BiLSTM) network is proposed in this paper. The IDBO algorithm improves the problem of low convergence accuracy and vulnerability to local optima in DBO. This is achieved by population initialization based on cubic chaotic mapping, amalgamating walrus optimization algorithm, and adaptive t-distribution perturbation strategy. The performance of the BiLSTM, IDBO-BiLSTM, and PCA-IDBO-BiLSTM shore power system load forecasting models are compared through case studies, revealing that the proposed PCA-IDBO-BiLSTM model demonstrates superior predictive capabilities.

Preface to Special Issue on Computational Advances in Short-Term Power System Operations

History: This paper has been accepted for the Operations Research Special Issue on Computational Advances in Short-Term Power System Operations.

Assessment of most critical project delays on a multi-stage transmission expansion plan using particle swarm optimization

Transmission reinforcement projects of a Transmission Expansion Plan can suffer implementation delays that can impact the short-term power system operation in unpredicted ways. These delays may lead project managers to paying particular care to the development of those plans proved to be most critical from this point of view. This paper proposes a multi-objective dynamic-programming based method to evaluate the impacts of such delays in a short-term transmission plan with respect to load shedding, reliability and losses. It uses the Binary Pareto-Optimal Particle Swarm Optimization as optimization tool. For the trade-off analysis and the final decision to be made, the paper proposes indices to rank the projects in the plan from most to least critical, should they be delayed, based on the severity of the impacts and the probability of occurrence, both on an annual basis and for the whole planning horizon. The proposed method has been tested on an actual Brazilian 40-bus subtransmission system for a 3-year and a 6-year planning horizon, and with the 24-bus IEEE RTS reliability test system for a 3-year planning horizon. To assess its accuracy and efficiency, the results are compared to those using the Non-dominated Sorting Genetic Algorithm II method (NSGA-II) and to the exhaustive search that considers every delay configuration, showing the same accuracy as the exhaustive search and better convergence properties than NSGA-II. The most critical projects are accurately ranked helping the project manager to decide which projects should receive higher attention to guarantee its construction on the planned date. The method obtained a reduction in computational time of 35% for the Brazilian system and of 42% for the IEEE RTS in relation to the exhaustive approach, demonstrating its efficiency.

Conditional Style-Based Generative Adversarial Networks for Renewable Scenario Generation

Day-ahead scenario generationof renewable power plays an important role in short-term power system operations due to considerable output uncertainty included. In this paper, a deep renewable scenario generation model using style-based generative adversarial networks followed by a sequence encoder network, is developed to generate accurate and reliable day-ahead scenarios directly from historical data through different-level scenario style controlling and mixing, thus achieving better characterization of renewable spatial-temporal dynamics. Meanwhile, the integration of meteorological information serving as conditions enables our model to precisely capture the complex diurnal pattern and seasonality difference of renewable power. From wind and photovoltaic power perspectives, the effectiveness of the proposed model is validated on two real-world datasets reflecting region aggregation level and distributed power station level respectively. Numerical results demonstrate the superiority of model performance through both the statistical and power system scheduling analysis, compared to three benchmarks.

Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting

The accurate prediction of power load is helpful to make reasonable power generation plans and scientific dispatching schemes and achieve the goal of energy saving and emission reduction.There are many factors influencing the power load, and there may be a nonlinear relationship between the power load and these influencing factors. A new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed in this article for the short-term prediction of power load. The fractional differential equation and fractional accumulation generation are integrated into MFGBM (q, r, N). Second, this paper improves the grey wolf algorithm to better optimize many parameters in the model. The algorithm is improved by using a chaotic Tent map to optimize the initial population composition, adding inertia weights to change the position vector of the grey wolf, and introducing a nonlinear function and Lévy flight to enhance local exploitation and global exploration ability. Finally, this paper selects the daytime and nighttime power loads in Australia and takes the electricity price, humidity, and temperature as the influencing factors to validate the prediction capability of MFGBM (q, r, N). Our findings indicate that MFGBM (q, r, N) is highly applicable to short-term power system prediction.

Construction and Application of Short-Term and Mid-Term Power System Load Forecasting Model Based on Hybrid Deep Learning

Power system load forecasting model plays an important role in all aspects of power system planning, operation and control. Therefore, accurate power load forecasting provides an important guarantee for the stable operation of the power grid system. This paper first analyzes the current status of power system load forecasting, and finds that there are still some deficiencies in the existing forecasting models. In order to make up for these shortcomings, this paper proposes construction of short-term and mid-term power system load forecasting models based on hybrid deep learning. In the data preprocessing part, this paper proposes to use the exponential weight moving average method to process missing values. The detection method of abnormal value is the GeneralizedESDTestAD(GESD). This paper analyzes the historical load data of a regional power grid and four industries, and proposes a short-term power system load forecasting model based on Bi-directional Long Short-Term Memory(BiLSTM); For mid-term load forecasting, this paper first uses random forest and Pearson correlation coefficient to select features. Then a hybrid deep learning model is constructed based on BiLSTM and random forest. After optimizing the parameters of the model, a mid-term power system load forecasting model based on hybrid deep learning is constructed. Finally, the benchmark models are selected for comparative experiments. The experimental results show that the MAPE of the proposed model is 2.36%, which is better than the benchmark models. This proves that the proposed hybrid model can effectively improve the accuracy of power system load forecasting.

Joint Scheduling Optimization of a Short-Term Hydrothermal Power System Based on an Elite Collaborative Search Algorithm

The joint scheduling optimization of hydrothermal power is one of the most important optimization problems in the power system, which is a non-linear, multi-dimensional, non-convex complex optimization problem, and its difficulty in solving is increasing with the expansion of the grid-connected scale of hydropower systems in recent years. In this paper, three effective improvement strategies are proposed given the shortcomings of the standard collaborative search algorithm, which easily falls into local optimization and weakening of global search ability in later stages. Based on this, an elite collaborative search algorithm (ECSA) coupled with three improvement strategies is established. On this basis, taking the classic joint scheduling problem of a hydrothermal power system as an example, the optimization model with the goal of the least pollutant gas emission is constructed, and the system constraint treatment method is proposed. In addition, five algorithms, i.e., ECSA, CSA, PSO, GWO, and WOA are used to solve the model, respectively. Through the comparison of results, taking the median as an example, the emission of polluting gases of ESCA is reduced by about 1.8%, 13.1%, 38.2%, and 11.2%, respectively, and it can be found that ECSA has obvious advantages in the convergence speed and quality compared with the other four algorithms, and it has a strong ability for global search and jumps out of the local optimal.

Short-term power system hourly load forecasting using artificial neural networks

Artificial neural networks (ANN) have been used for many application in various sectors. The learning property of an ANN algorithm in solving both linear and non-linear problems can be utilized and applied to different forecasting problems. In the power system operation load forecasting plays a key role in the process of operation and planning. This paper present the development of an ANN based short-term hourly load forecasting model applied to a real data from MIBEL – Iberian power market test case. The historical data for 2012 and 2013 ware used for a Multilayer Feed Forward ANN trained by Levenberg-Marquardt algorithm. The forecasted next day 24 hourly peak loads and hourly consumptions are generated based on the stationary output of the ANN with a performance measured by Mean Squared Error (MSE) and MAPE (Mean Absolute Percentage Error). The results have shown good alignment with the actual power system data and have shown proposed method is robust in forecasting future (short-term) hourly loads/consumptions for the daily operational planning.

Simultaneous optimal short term SCUC solution and power system redesign with real gap decomposition technique

In this work, firstly, a decomposition technique is proposed and applied to solve the Security Constrained Unit Commitment problem (SCUC). A recently developed model for optimal short term programming of power generation in thermoelectric plants including transmission constraints is adopted. Real gap is computed at the final solution. Secondly, a method to analyze results and redesign the system structure is also proposed. By applying this method, critical lines and generators can be detected, and then, network improvements can be suggested by either eliminating or duplicating lines or generators. With the aim of studying the efficiency of the proposed method, two systems of different sizes are considered. The efficiency of the proposed technique is compared with the direct resolution of the original model and a linearly approximate model. The proposed decomposition technique presents a good performance: optimal solutions are obtained within small real gaps and efficient computing times. In power systems, the SCUC problem is solved every 60 minutes or less; therefore, the resolution time sought must be much less than that. Without applying the decomposition technique, the resolution of the SCUC problem takes several hours to reach an acceptable gap; and instead, when applying the decomposition technique; smaller gaps can be obtained in a few minutes.

Оптимізація диспетчеризації генеруючих потужностей енергосистеми за умови обмеження генерації вітрових та сонячних електростанцій

To solve the actual task of finding optimal generation mix and dispatch of thermal and nuclear power units, and hydro units of hydro pumped storages of Ukraine to cover day load power profile according to one of possible wind and solar generation curtailment mode the modification of mathematical MIP model is proposed. There are three modes of wind and solar generation curtailment revised in the article: 1) absolute maximum generation curtailment, 2) single for whole day decreased load factor, and 3) one per one hour decreased load factor. The possibility to allocate an exogenously determined level of frequency containment reserves (secondary reserves) on thermal power units is realized in the MIP model. The calculation’s results analysis shows that among revised wind and solar generation curtailment methods the method 2) is most appropriate in the short term because only administrative measures implementation is required, which could be put into force with appropriate legislation and does not require essential investments or implementation of complicated technical measures. The additional possible positive effect caused by the implementation of method 2) is it makes background for participation wind and solar generation in the ancillary services market and intraday balancing. In the middle term, the gradual implementation of method 3) is the most appropriate decision because a more stable power system balancing mode (minimum import/export amounts) could be provided. Moreover, extra nuclear power units and fewer coal thermal power units could be dispatched that is decreases hazard pollutions and carbon emission. The MIP model is written using MathProg language, a freeware version of AMPL. As a solver, the GNU GLPK program is used. The overall time for one calculation with standard table PC is about 30 seconds. MIP model could be used both for short-term power system optimal dispatch and for long-term national generation mix development studies under the growth rates of renewable installed capacities. Keywords: power system, daily load profile, robust decisions, mixed linear-integer problem, frequency containment reserve

Research on Economic Evaluation Methods of Offshore Oil Multi-Platform Interconnected Power System Considering Petroleum Production Characteristics

Offshore oil multi-platform interconnected power system is developing rapidly. The proposal of an effective economic evaluation method that fits the actual production situation of offshore oilfields is very meaningful for the planning and construction of multi-platform interconnected power systems. This article proposes the electric depreciation, depletion, and amortization (DD&A) barrel oil cost S and maximum expected benefit per unit power generation Ie as economic indicators, considering the actual production characteristics and life cycle of the oil field. In order to build a complete economic evaluation system, this article also introduces the N−1 pass rate ηN−1, voltage qualification rate γ, power supply reliability ASAI (Average Service Availability Index), and other reliability indicators to evaluate the offshore power system. When calculating the weight of the indicators, analytic hierarchy method (AHP) was applied to calculate subjective weights, and an entropy method was applied to calculate objective weights. To unify the two weights, the ideal point method is proposed to obtain compound weights. Finally, this article selects an offshore oil field in Bohai Bay, China as example, and analyses short-term small-scale, long-term large-scale, and actual power system as calculation examples in different planning periods. The analysis result verifies the effectiveness of the economic evaluation method.

Coordinated Control of Air-Conditioning Loads for System Frequency Regulation

The increasing penetration of renewable generation presents challenges for system frequency regulation due to short-term power fluctuations and system inertia reductions. This article presents a coordinated control strategy for frequency regulation in which inverter air-conditioning (IAC) units are used to perform primary frequency regulation (PFR) and fixed frequency air-conditioning (FFAC) units are used to perform secondary frequency regulation (SFR). In PFR, the regulation power is provided by adjusting the setpoints of the IAC units. A random number generation method is proposed to stochastically trigger IAC units based on the frequency deviation in real time. Furthermore, a recovery method is presented to stably restore the IAC units to their initial operating states after regulation. In SFR, a constant equivalent duty ratio method is raised to keep the regulation power stable over a long instruction interval. Based on this, the transforming time interval method is presented to determine the ON/OFF status of FFAC units to provide the required regulation power. Additionally, a recovery method for FFAC units is proposed to mitigate the power rebound after regulation. The proposed control strategy achieves an improved frequency regulation effect with fewer communication demands. Dynamic simulations in a six-machine two-area system and an isolated microgrid with wind power verify the effectiveness of the proposed control strategy.

SHORT-TERM HYDROTHERMAL COORDINATION BASED ON PRIMAL-DUAL INTERIOR POINT METHOD

This paper proposes a Primal-Dual Interior Point (PDIP) algorithm for solving short-term hydrothermal power system (STHTS) scheduling problem considering power losses in transmission system, forecasted wind power and transmission line constraint. A mathematical model for solving the problem of STHTS has been developed and implemented in the MATLAB software package. The PDIP forms Lagrange function by adding an internal (barrier) penalty function, and converting all inequality constraints to equality constraints, and then Newton's iterative method is used to solve the system equations. The main advantages of the PDIP method are the large scale type and the ability to solve optimization problems with sparse matrices. The effectiveness of the proposed PDIP has been previously tested on different hydrothermal power systems and the obtained test results have been compared to those from other methods in the literature. The main purpose of this paper is to highlight the impact of transmission line constraint on the operating costs of the thermal power plants, i.e. that it should be included in the mathematical model of STHTS.

Mathematical models and algorithms of optimal load management of electrical consumers

The article investigated the static, dynamic and metrological characteristics of new magnetoelastic force sensors taking into account the distribution of Load profile smoothing based on optimal power management is considered to be one of the main ways to ensure efficient operation of energy systems in the short-term planning. Smoothing load profile with a view to reducing costs can be implemented on the ways in which consumers could be affected by administrative and economic measures. Administrative measures are associated with the forced restriction of consumer loads in certain intervals of the planning period. On the one hand, these measures provide benefits to the power system by smoothing load profile, and on the other hand, they cause damage to consumers. Ultimately, in some cases, the damage may be greater than the benefits as a whole to the power system. Therefore, it is advisable to use them in conditions of shortage of power and electricity in the power system. Optimal planning of short-term regimes of power systems according to received, after alignment, rigid load profile can be carried out by traditional methods. The solution of such a problem can be initially carried out under conditions of non-rigid load profile resulting from the directive use of administrative and economic measures carried out with the help of specially developed models. In this regard, the paper proposes a mathematical model of the problem of optimizing load profile of regulated electrical consumers with optimal planning of short-term power system modes, an algorithm for optimal planning of a short-term power system mode with optimizing load profile of regulated power consumers. And also, algorithms are proposed for accounting for simple and functional constraints in the form of equalities and inequalities when optimizing load profile. The effectiveness of the described algorithm for optimizing the short-term mode of the power system, taking into account the optimal load control of adjustable electric locomotives, has been studied using the example of optimal coverage of the load profile of power system, which contains two consumers with adjustable load profile, and two TPPs. Based on the calculated experimental studies computational qualities, it was determined that the proposed mathematical model of the problem is adequate, the developed algorithms for optimal planning of short-term power system modes with optimization of load profile of regulated electric consumers and taking into account various types of limitations have high computational qualities.

Mathematical Models and Algorithms of Optimal Load Management of Electricity Consumers

Load profile alignment based on optimal power consumption management is considered to be one of the main ways to ensure efficient operation of energy systems in the short-term planning. Alignment load profile with a view to reducing costs can be implemented with the aid of consumers’ involvement by administrative and economic measures. Administrative measures are associated with the forced restriction of consumer loads in certain intervals of the planning period. On one hand, these measures provide benefits to the power system by alignment load profile, and on the other hand, they cause detriment to consumers. Ultimately, in some cases, for the whole power system, the detriment may be greater than the benefits. Therefore, it is advisable to use administrative measures in conditions of shortage of power and electricity in the power system. Optimal planning of short-term regimes of power systems according to rigid load profile received after alignment can be carried out by traditional methods. The solution of such a problem ought to be initially carried out under conditions of non-rigid load profile resulting from the directive use of administrative and economic measures carried out with the help of specially developed models. In this regard, the paper proposes a mathematical model of the problem of optimizing load profile of regulated electricity consumers to be used for optimal planning of shortterm power system modes, an algorithm for optimal planning of a short-term power system mode with optimizing load profile of regulated power consumers. Also, algorithms are proposed for accounting for simple and functional constraints in the form of equalities and inequalities when optimizing load profile. The effectiveness of the described algorithm for optimizing the short-term mode of the power system, taking into account the optimal load control of adjustable electricity consumers, has been studied using the example of optimal coverage of the load profile of power system, which contains two consumers with adjustable load profile, and two TPPs. Based on the calculated-and-experimental studies, it was determined that the proposed mathematical model of the problem is adequate, while the developed algorithms for optimal planning of short-term power system modes with optimization of load profile of regulated electricity consumers and taking into account various types of limitations are of high computational qualities.

How detailed value of lost load data impact power system reliability decisions

The value of lost load (VOLL) is an essential parameter for power system reliability. It represents the cost of unserved energy during power interruptions. Various studies have estimated this parameter for different countries and more recently, for different interruption characteristics – such as interruption duration, time of interruption and interrupted consumer. However, it is common practice in system operation and the literature to use only one uniform VOLL. Our theoretical analysis shows that using more-detailed VOLL data leads to more cost-effective transmission reliability decisions. Using actual consumer- and time-differentiated VOLL data from Norway, Great Britain and the United States, numerical simulations of short-term power system reliability management indicate a potential operational cost decrease of up to 43% in a five-node network, and between 2% and 18% in a more realistic 118-node network – mainly because of lower preventive redispatch costs in response to lower expected interruption costs. However, changed reliability practices could lead to opposition, if some consumers are disproportionately interrupted and not adequately compensated. Although the first policy measures to collect more detailed and harmonized VOLL data have been taken, future policy should improve transmission-distribution coordination and enable the participation of all consumers in curtailment programs, through smart meters and smart appliances.

Using the Matpower Optimal Scheduling Tool to Test Power System Operation Methodologies Under Uncertainty

Short-term power system operational planning problems that consider multi-stage uncertainties pose significant challenges, not only in the design of tractable optimization frameworks for implementing them, but also in the testing and benchmarking of such frameworks. This paper presents an implementation using the open-source M atpower Optimal Scheduling Tool (MOST) to study and compare a stochastic day-ahead, security-constrained unit commitment problem with a more traditional deterministic approach. The comparison is based on a testing methodology for day-ahead plans designed to produce expected performance estimates with minimal biases from modeling assumptions. Emphasis is given in the proposed stochastic approach to explicit modeling of the operational characteristics of the technologies available, their spatial and temporal coupling, and the regulatory constraints that assure reliability and adequacy. The problem formulations and testing methodology are described and simulation results from MOST are presented, with discussion of implications for future market design. All of the code and data to replicate the simulations is provided online.

Short-term Power System Maintenance Scheduling with Imperfect Repair

Most of the existing research in short-term power system maintenance scheduling assumes that repair is minimal when equipment fails. However, this may not always be the truth because sometimes repair after failure is imperfect. This paper investigates the impact of imperfect repair on power system maintenance scheduling. Unlike previous research which didn’t consider imperfect repair, a condition-based maintenance optimization model is proposed. The model focuses on equipment deterioration failure. Firstly, a new equipment state-space diagram under imperfect repair is given. And then, the availability of power equipment considering maintenance schedule and imperfect repair is derived using the Markov process theory. Based on that, in order to determine the optimal equipment maintenance schedules, the expression of system outage cost is given. Finally, system outage cost is minimized to optimize equipment maintenance schedules. A case study is used to illustrate the effectiveness and significance of the proposed model.

A Short-term Load Forecasting Method Based on Fuzzy Neural RBF Network Adaptive Control

According to historical load data of the power grid in a certain area, by which analyze this area’s power load characteristic and consider the load forecasting influence factors such as the date type, temperature, weather conditions in the first. In view of the load has a certain objective laws, but which has a lot of randomness and uncertainty, applying one kind new based on the RBF (Radial Basis Function) Neural Fuzzy Inference to carry on short-term load forecasting. By programming with MATLAB to carry on short-term power system load forecasting, carry on the short-term load forecast experiment to the practical grid and draw the forecasting result curves. The result indicated that the RBF Adaptive Neural Fuzzy Inference of the forecast accuracy is satisfied with the verification of this method is effective and practical.

Impact of gate closure time on the efficiency of power systems balancing

This paper focuses on market design options for operational balancing management in self-dispatch electric power systems. In particular, it investigates the most relevant timing for the balancing gate closure, when competitors' decisions on the setting of controllable assets are neutralized and this responsibility is simultaneously transferred to the system operator. This discussion is central in the development and implementation of the European Electricity Balancing Guideline. Based on a multi-level simulation tool with a realistic modelling of short-term power system operations, this paper proposes the first quantitative assessment of postponing the balancing gate closure time from 1 h to 15 min ahead of the imbalance settlement period. For different environments (energy mix, power plant capabilities, outages, etc), the results highlight that postponing the balancing gate closure time from 1 h to 15 min increases the operational cost of the system. Based on robust and scalable results, we show that this difference is mainly due to a better coordination of the available resources by the central decision maker.