Economy Of Power System Research Articles

Development and Implementation of a Novel Optimization Algorithm for Reliable and Economic Grid-Independent Hybrid Power System

Recently, fast uptake of renewable energy sources (RES) in the world has introduced new difficulties and challenges; one of the most important challenges is providing economic energy with high efficiency and good quality. To reach this goal, many traditional and smart algorithms have been proposed and demonstrated their feasibility in obtaining the optimal solution. Therefore, this paper introduces an improved version of Bonobo Optimizer (BO) based on a quasi-oppositional method to solve the problem of designing a hybrid microgrid system including RES (photovoltaic (PV) panels, wind turbines (WT), and batteries) with diesel generators. A comparison between traditional BO, the Quasi-Oppositional BO (QOBO), and other optimization techniques called Harris Hawks Optimization (HHO), Artificial Electric Field Algorithm (AEFA) and Invasive Weed Optimization (IWO) is carried out to check the efficiency of the proposed QOBO. The QOBO is applied to a stand-alone hybrid microgrid system located in Aswan, Egypt. The results show the effectiveness of the QOBO algorithm to solve the optimal economic design problem for hybrid microgrid power systems.

Inaugural editorial: Energy Conversion and Economics

Inaugural editorial: <i>Energy Conversion and Economics</i>

Solving Power System Economic Dispatch Problem Based on Genetic Algorithm

With the rapid development of the world economy, the demand for power load is also growing rapidly. The power system economic dispatch problem studied in this paper is to reduce the fuel cost of generator units on the premise of satisfying some series of constraints of power system. In this paper, genetic algorithm is adopted as the solution strategy for power system economic dispatch problem, and the penalty function is used to deal with the equality constraint condition in the power system economic dispatch problem. Finally, we use the genetic algorithm to solve the IEEE-9 notes power system economic dispatch problem and give the corresponding simulation results. The simulation results show that the genetic algorithm can solve the power system economic dispatch problem effectively.

Neural Nework-Based Time Series Methods for Load Forecasting

Load forecast plays an important role in power system operation and control. Significant contribution in power system economics may also be observed. Many decisions of the power system depend on the future load demand. The accuracy of STLF is necessary for the optimal and economical operation of the power systems. This paper presents a new approach to STLF. In this paper, time series methods are presented on the basis of neural networks. The time series methods are included autoregressive, nonlinear autoregressive, and non-linear autoregressive with external inputs (narx). The comparative results are presented with the ANN. In this paper, the narx method gives more efficient and accurate results than other methods.

WKS‐type distributionally robust optimisation for optimal sub‐hourly look‐ahead economic dispatch

Renewable energy sources, particularly wind power, are being increasingly deployed in power systems to reduce environmental contamination. However, the uncertainty of wind power significantly influences power system economy. Therefore, this study presents Wiesemann–Kuhn–Sim (WKS)-type distributionally robust optimisation for performing an optimal two-stage sub-hourly look-ahead economic dispatch considering uncertain wind power. The dispatch considers the scheduling of thermal generators, wind power generators, and fast-response resources. The optimisation aims at minimising the expected total operational cost, including the costs of generation, generation shedding, and wind power curtailment. The hourly stage determines the thermal generation that can withstand the worst-case wind power distribution. The sub-hourly stage schedules the fast-response operations to correct the hourly dispatch. To characterise the uncertainty, a novel WKS-format ambiguity set that can obtain wind power distribution from historical data is constructed using the lifting theorem. Based on the ambiguity set and linear decision rule, a general lemma is employed to convert the model into a tractable conic optimisation that can be directly solved. Experimental results demonstrate the effectiveness of the presented approach compared to adjustable robust optimisation and sample-based stochastic optimisation.

Lightning attachment procedure optimization algorithm for nonlinear non-convex short-term hydrothermal generation scheduling

Short-term hydrothermal scheduling (STHS) is considered an important problem in the field of power system economics. The solution of this problem gives the hourly output of power generation schedule of the available hydro and thermal power units, which leads to minimization of the total fuel cost of thermal units for a given period of a time. The optimal generation of STHS is considered as a complicated and nonlinear optimization problem with a set of equality and inequality constraints such as the valve point loading effect of thermal units, the power transmission loss and the load balance. This paper proposes lightning attachment procedure Optimization (LAPO) algorithm for solving the nonlinear non-convex STHS optimization problem in order to minimize the operating fuel cost of thermal units with satisfying the operating constraints of the system. The performance of LAPO algorithm is validated using three different test systems considering the valve point loading effects of thermal units and the power transmission losses. The obtained results prove the effectiveness and superiority of LAPO algorithm for solving the STHS problem compared with other well-known optimization techniques.

A Reliable and Economic Power System Dispatch

A Reliable and Economic Power System Dispatch

A novel single-ended fault location method for long-distance HVDC transmission lines based on cross-correlation analysis

The transmission line, as the faultiest device in power system, cross many complex areas which will cause worse work condition. This is, both the rate of faulty and the difficulty of the manual fault location are raised. While accurate fault location method could reduce the workload and outage cost, increase the speed of repairing and improve both the reliability and economics of power system. Therefore, it is essential for HVDC systems which have much longer transmission lines. The existing fault location device of HVDC transmission lines are based on the principle of transient travelling-wave. However, due to the interference caused by on-off of power electronic devices and signal resonances, the single-ended travelling-wave method can not be applied in HVDC. The characteristics of clutter interferences of HVDC system and the coupling between double pole lines are analyzed in paper. A novel single-ended travelling-wave fault location method for HVDC transmission lines based on the correlation between two pole lines is proposed. The signal of the non-fault pole line is applied as reference signal; the method suppresses interference through the cross-correlation calculation between the fault and the no-fault pole line. So the reflected wave is enhanced and the identification reliability of the reflected wave is improved. The validity of the method is verified by simulation and on-site data analysis.

The Power Simulation of Water-Cooled Central Air-Conditioning System Based on Demand Response

With rapid development of smart grid, terminal power load is paid more and more attention, especially that of air conditioning system, which constitute an integral part of summer peak electricity load. In this paper, through controlling indoor air temperature deduced by equivalent thermal parameters-model, daily energy consumption and energy consumption during peak hours can be adjusted and reduced to realize demand side management. To reduce peak load of grid and ensure stable and reliable operation of grid, four temperature control strategies, including constant setting temperature control strategy, pre-cooling control strategy, curtailment control strategy and improved curtailment control strategy were proposed. Meanwhile, three operation schemes of the chiller were also employed. Furthermore, considering the power consumption and curtailment, five indicators including hourly average load, hourly average load ratio, energy saving ratio, load-shedding ratio during peak hours and coefficient of performance (COP) have been proposed to evaluate the performance. Results showed that the operation scheme of operating numbers control obtained better effect. Energy saving ratio was highest of 32.5% with curtailment control strategy, while load-shedding ratio during peak hours was optimal with improved curtailment control strategy. However, COP was maximum at 3.66 when pre-cooling control strategy was employed. This study can guide the operation of water-cooled air conditioning system through energy saving ratio and COP, also it has more benefits on economy of power system by hourly average load and load-shedding ratio.

Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.

Reinforced Deterministic and Probabilistic Load Forecasting via $Q$ -Learning Dynamic Model Selection

Both deterministic and probabilistic load forecasting (DLF and PLF) are of critical importance to reliable and economical power system operations. However, most of the widely used statistical machine learning (ML) models are trained by optimizing the global performance, without considering the local behaviour. This paper develops a two-step short-term load forecasting (STLF) model with Q-learning based dynamic model selection (QMS), which provides reinforced deterministic and probabilistic load forecasts (DLFs and PLFs). First, a deterministic forecasting model pool (DMP) and a probabilistic forecasting model pool (PMP) are built based on 10 state-of-the-art ML DLF models and 4 predictive distribution models. Then, in the first-step of each time stamp, a Q-learning agent selects the locally-best DLF model from the DMP to provide an enhanced DLF. At last, the DLF is input to the best PLF model selected from the PMP by another Q-learning agent to perform PLF in the second-step. Numerical simulations on two-year weather and smart meter data show that the developed STLF-QMS method improves DLF and PLF by 50% and 60%, respectively, compared to the state-of-the-art benchmarks.

An intelligent protection scheme to deal with extreme fault currents in smart power systems

In modern power systems, the generation capacity is increasing significantly for responding to high electric energy demands. This generation expansion directly affects the level of short circuit in transmission systems and increases the fault current magnitude, which may pass the breaking capacity of circuit breakers. To deal with extreme fault currents in power systems, an intelligent protection scheme is proposed in this paper, which removes the necessity of installing expensive fault current limiters. This protection scheme uses communication capabilities defined in IEC 61850 to design smart cascading switching actions to isolate the faulty part of the system. In fact, it is a supervisory protection algorithm in addition to the conventional distance protection, which is the main protection of HV transmission systems. This protection is activated if the fault current is higher than breaking capacity of circuit breakers; otherwise, the conventional distance protection is responsible to clear the fault. Numerical results obtained from implementing the proposed protection algorithm in a 400 kV system demonstrate the effectiveness of this method in terms of fault clearing time, stability margin improvement and power system economy.

Security-Constrained Unit Commitment With Natural Gas Pipeline Transient Constraints

The interdependencies of power systems and natural gas networks have increased due to the additional installations of more environmental-friendly and fast-ramping natural gas power plants. The natural gas transmission network constraints and the use of natural gas for other types of loads can affect the delivery of natural gas to generation units. These interdependencies will affect the power system security and economics in day-ahead and real-time operations. Hence, it is imperative to analyze the impact of natural gas network constraints on the security-constrained unit commitment (SCUC) problem. In particular, it is important to include natural gas and electricity network transients in the integrated system security because the impacts of any disturbances propagate at two distinctly different speeds in natural gas and electricity networks. Thus, analyzing the transient behavior of the natural gas network on the security of natural gas power plants would be essential as these plants are considered to be very flexible in electricity networks. This paper presents a method for solving the SCUC problem considering the transient behavior of the natural gas transmission network. The applicability of the presented method and the accuracy of the proposed solution are demonstrated for the IEEE 118-bus power system, which is linked with the natural gas transmission system and the results are discussed in this paper.

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.

Research on Influencing Factors of Line Loss Based on Multi Model Analysis

Line loss is an important factor to measure the economy of power system. In order to explore the main factors affecting line loss, the line loss data of 60 stations in a city are screened out by using power information collection system. With the voltage level data as the index, the stations are clustered by the fuzzy clustering algorithm to form effective differential grouping. According to different types of stations, the curve similarity method and regional factor analysis method are used to analyze the weather conditions and the influence degree of the station factors on line loss. The main influencing factors of line loss are obtained. According to the analysis results of each type of stations, the gray correlation analysis algorithm is used to rank the significant factors according to the influence degree. Taking a certain area line loss data as an example, the influencing factors of line loss abnormality are studied. The results show that according to the characteristics of each type of platform area, the model can analyze the factors of each type of platform area and judge the influencing factors at different levels, so as to achieve the effect of targeted and hierarchical governance for different regions.

A deep spatial-temporal data-driven approach considering microclimates for power system security assessment

With the integration of renewable energy and microclimate-sensitive loads, secure and economic power system operation is becoming an increasingly important and complex problem. Therefore, based on big data from power systems and meteorological systems, a deep spatial-temporal data-driven model is proposed to predict and detect power system security weak spots during a future period. First, microclimates are considered in the proposed model. Then, a deep neural network structure is designed to extract deep features layer by layer for security weak spot detection. Furthermore, model simplification and parallelism as well as data parallelism are applied. Finally, the proposed model is evaluated based on the Guangdong Power Grid in China. The simulation results demonstrate that (1) power system security weak spots have spatial-temporal and microclimate-sensitive characteristics; (2) the deep model considering microclimates can greatly improve the task accuracy of online applications; and (3) simplification and parallelism can significantly enhance the training efficiency.

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Economic load dispatch (ELD) became recently a mandatory tool for reliable and economic power system operation. ELD improves the security of the power supply while minimizing the generation costs. Daily load curve imposes conflicting requirements on ELD scheme, such as fulfilling demand during peak periods while limiting the cost. Demand response (DR) is an intelligent approach that ensures load demand fulfillment without introducing additional costs. DR allows shifting the electricity consumption to the non-peak periods. Therefore, DR could be considered as a virtual power plant. ELD is usually solved as nonlinear constraint optimization problem. Three simple, fertile, and robust meta-heuristic optimizations are competing in this article for solving ELD incorporating DR as virtual power plant. These are ant lion optimizer (ALO), particle swarm optimization (PSO), and artificial immune system (AIS). The objectives are minimizing the overall cost, system losses, and emission levels. Variety of distinctive study systems are considered to test the functionality and feasibility of the proposed algorithms; they are 6-bus, 30-bus, and 118-bus IEEE systems. MATLAB environment is used for coding ALO, PSO, AIS, and the systems under concern. The comprehensive results show that incorporating DR in ELD reduces the costs/losses/emissions without affecting the power system security and reliability. The comparative results verify the robustness and reliability of ALO in minimizing the overall operating cost and emission levels of the power systems under concern while confining with the constraints. Moreover, they show that ALO has the highest accuracy and convergence efficiency while enjoying the reduced computation storage and execution time.

TCM considering EIZs' integration

Transmission congestion threatens the reliability and economy of power systems. It becomes more severe with the increase of load demands and peak−valley differences. In recent years, energy internet zones (EIZs) have attracted wide attention, and many demonstration projects have been developed. EIZs connect the supply and consumption of multi-energy like electricity, gas, heat, and cooling. Thus, EIZs’ electricity load demands can be flexibly adjusted by controlling their multi-energy flows. The controlling method should consider not only demand response but also heat, cooling, and gas energy balance. The integration of EIZs provides a new solution for transmission congestion management (TCM). First, an EIZ's optimal controlling strategy considering its multi-energy flow requirements was proposed. Then, the optimal re-dispatch problem for TCM was studied, taking into account EIZs' and traditional generators' adjustment costs. Re-dispatch determines the output adjustment schedules and compensation prices, while the EIZ's controlling strategy controls multi-energies' supply and consumption. A simplified Songjiang transmission system in Shanghai was adopted to illustrate the proposed methods. It was proven that the EIZs' integration could reduce the re-dispatch costs in TCM and increase EIZs' profits.

Battery Energy Storage Selection Based on a Novel Intermittent Wind Speed Model for Improving Power System Dynamic Reliability

Owing to intermittency of wind power and slow ramp rates of conventional generators, a considerable amount of wind energy cannot be effectively utilized during frequency control processes. This paper proposes a technique for power system planners and operators to select or commit power capacity and energy capacity of battery energy storage system (BESS) for mitigating the effects of intermittent wind speeds on reliability and economics. A novel procedure is proposed to determine intermittent wind speeds based on the chaos theory and intermittency-related parameters. The frequency-related reliability and economic indices of a power system with BESS are formulated based on frequency control processes. The effects of BESS on dynamic reliability and economics of a practical power system are investigated using the proposed techniques and models.

Probabilistic Wind-Power Forecasting Using Weather Ensemble Models

During the past one to two decades, the probabilistic forecasting of a wind-power generation has been regarded as a necessary input to decisions made for the purpose of reliable and economic power systems operations, especially since the penetration of the renewable energy has begun to grow rapidly. Probabilistic forecasting differs from traditional deterministic forecasting in which it takes uncertainty into account. This paper proposes a modified nonparametric method for constructing reliable prediction intervals (PIs). The lower upper bound estimation (LUBE) method is adapted to construct PIs for the wind-power generation, based on the ensemble wind-speed data from the numerical weather prediction system of the Central Weather Bureau of Taiwan. The charged system search (CSS) is used to adjust parameters in LUBE. The performance of the proposed method is examined using datasets from several wind farms in Taiwan. Simulation results demonstrate that the quality of PIs output by the proposed model significantly exceeded that of those constructed using the persistence model with a 1-h-ahead time horizon.