Operation Planning Of Power Systems Research Articles

Integrated data‐driven framework for fast SCUC calculation

Security-constrained unit commitment (SCUC) is one of the most important optimisation problems for operation planning of power systems. Indeed, with the fast expansion of power grids and the increasing growth of heterogeneous market participants in electricity markets, the day-ahead SCUC continuously encounters significant performance challenges. To improve the computational performance of SCUC and achieve solutions of good quality, an integrated framework which combines a data-driven approach and a variable-aggregation method is presented in this study. The variable-aggregation method effectively approximates the original network security constraints with a reduced number of variables. Moreover, as aggregated constraints could reduce the feasible region of the original SCUC and potentially degrade solution quality for certain SCUC instances, it is preferable to only apply such an aggregation approach towards difficult SCUC instances. Therefore, a data-driven classification method, by adopting relevant SCUC input data as features, is integrated to first predict whether a SCUC instance is ‘easy’ or ‘hard’. To this end, the integrated framework could improve the overall performance of SCUC instances, by and large, in terms of computational efficiency and solution quality. Numerical results illustrate the effectiveness of the proposed integrated framework.

Towards leveraging discrete grid flexibility in chance-constrained power system operation planning

This paper considers the integration of grid flexibility in the chance-constrained power system operation planning framework. The particular challenge addressed comes from the discrete nature of the respective controls, such as breaker positions defining the topology of the network. We consider a template short-term operation planning problem statement, seeking to enable N-1 secure operation over a distribution of power injections. We use a scenario-based approach to determine a planning decision and rely on theoretical results to compute an upper bound on the probability of being able to meet the N-1 criterion in operation. We also estimate the actual value of this probability through Monte Carlo simulation. Our results indicate that both the bound and the actual value consistently decrease when increasing the size of the considered scenario set, even if the bound is quite conservative. Moreover, we showcase that further from economic efficiency, grid flexibility can lead to gains in operational reliability.

Model Order Reduction of Multi-Terminal Direct-Current Grid Systems

This paper proposes a methodology to develop a linear low-order model of a multi-terminal direct-current (MTDC) system connected to practically-sized offshore wind farm systems. The method individually linearizes the model of wind farm systems and subsequently performs model order reduction (MOR) using iterative rational Krylov algorithm (IRKA). This is, therefore, a modular approach and expandable to any number of wind farm connections. On the other hand, the model order and non-linearity of other components are retained. Effectiveness of the proposed method is demonstrated on an MTDC system connected to two large-scale wind farm models by comparing the responses obtained from the full order model (FOM) and reduced order model (ROM). From the simulation studies, it can be seen that the ROM obtained from proposed method is able to maintain high degree accuracy of FOM responses at a much faster simulation time, and hence this can help power system operation planning. Various control functionalities and strategies, different operating conditions and faults of the MTDC system are also considered to investigate robustness of the proposed method.

Frequency stability improvement in wind‐thermal dominated power grids

With the proliferation of intermittent renewable energy sources, power systems need to withstand an increasing number of disturbances that may affect system frequency. In this paper, we focus on the effects of high penetration level of wind turbine-generators (WTG) on the power system operational planning from the frequency point of view. Specifically, an exact formulation for the minimum frequency calculation is presented to increase accuracy and speed of analyses. Then, the effect of WTG's frequency response on the power system frequency stability is investigated to compute the maximum acceptable generation outage as a function of penetration level. Finally, as an application to power system operational planning, the obtained frequency dynamics of the power system and the wind turbine frequency response are integrated into the unit commitment problem. Simulations are performed on the IEEE 39-bus test system to study the effects of the WTGs on the frequency stability of the system. Results illustrate a more secure and reliable operation of the power system when the wind turbines participate in the frequency regulation task. Moreover, including the frequency response of the wind turbines in the unit commitment problem can reduce the ancillary cost, meanwhile increase the frequency stability of the system.

Uncertainty Quantification for Optimal Power Flow Problems

AbstractThe need to de‐carbonize the current energy infrastructure, and the increasing integration of renewables pose a number of difficult control and optimization problems. Among those, the optimal power flow (OPF) problem—i.e., the task to minimize power system operation costs while maintaining technical and network limitations—is key for operational planning of power systems. The influx of inherently volatile renewable energy sources calls for methods that allow to consider stochasticity directly in the OPF problem. Here, we present recent results on uncertainty quantification for OPF problems. Modeling uncertainties as second‐order continuous random variables, we will show that the OPF problem subject to stochastic uncertainties can be posed as an infinite‐dimensional L2‐problem. A tractable reformulation thereof can be obtained using polynomial chaos expansion (PCE), under mild assumptions. We will show advantageous features of PCE for OPF subject to stochastic uncertainties. For example, multivariate non‐Gaussian uncertainties can be considered easily. Finally, we comment on recent progress on a Julia package for PCE.

A Generic Framework for Analytical Probabilistic Assessment of Frequency Stability in Modern Power System Operational Planning

In view of modern power system characterized by significant inertia reduction and booming uncertainty, this letter proposes an important operational planning tool to comprehensively analyze frequency stability including both steady-state frequency and rate of change of frequency issues in a probabilistic manner for the first time. The proposed generic framework can tackle various frequency-related uncertainties and accommodate different system frequency response models. The effectiveness and efficiency of the proposed analytical probabilistic assessment are demonstrated by comparing with numerical scenario-based simulation.

Combined Emission Economic Dispatch of Power System in Presence of Solar and Wind Using Flower Pollination Algorithm

ED (Economic Dispatch) problem is one of the vital step in operational planning. It is a nonconvex constrained optimization problem. However, it is solved as convex problem by approximation of machine input/output characteristics, thus resulting in an inaccurate result. Reliable, secure and cheapest supply of electrical energy to the consumers is the prime objective in power system operational planning. Increase in fuel cost, reduction in fossil-fuel assets and ecological concerns have forced to integrate renewable energy resources in the generation mix. However, the instability of wind and solar power output affects the power network. For solution of such solar and wind integrated economic dispatch problems, evolutionary approaches are considered potential solution methodologies. These approaches are considered as potential solution methodologies for nonconvex ED problem. This paper presents CEED (Combined Emission Economic Dispatch) of a power system comprising of multiple solar, wind and thermal units using continuous and binary FPA (Flower Pollination Algorithm). Proposed algorithm is applied on 5, 6, 15, 26 and 40 thermal generators by integrating several solar and wind plants, for both convex and non-convex ED problems. Proposed algorithm is simulated in MATLAB 2014b. Results of simulations, when compared with other approaches, show promise of the approach.

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.

Loading margin sensitivity analysis in systems with significant wind power generation penetration

The main objective of this paper is to evaluate the impacts in electrical networks, particularly on the voltage stability margins, due to the presence of Wind Generation (WG) and its intermittent characteristic. For this purpose, linear and quadratic sensitivities of the Loading Margin (LM) with relation to the wind speed are calculated, and then utilized to estimate possible alterations in the LM due to perturbations in the wind speed of the wind farms. To include the variable wind speed in the sensitivity analysis of the LM two types of wind generators are mathematically modeled and then incorporated into the power flow formulation. Lastly, three case studies are performed, where accurate estimates of the LM are obtained for wind speed perturbations in the wind farms, demonstrating the effectiveness of the proposed methodology and its potential to assist in the real-time operation and the operation planning of power systems with WG.

Computational Techniques for three-phase Load Flow Analysis

This paper aims to compute a three-phase load flow for balanced load and unbalanced loads conditions. Unbalanced loading can occur if the power demand of each phase is not equal, this happens because the load usage of each phase varies. Three-phase load flow is a study in the electric power system that will provide information on voltage, phase angle, current, power and power losses of each bus on phases a, b and c. Three phase power flow is carried out for operation planning and control of three-phase electric power systems. The imbalance in the electric power system will affect the use of electrical energy, that is, there will be a decrease in voltage received by consumers. Three-phase load flow computing will be done using the Etap version 12.0 software which will be tested on the IEEE 6 bus system. The computational results show that the unbalanced load flow converges on the 2nd iteration of the bus. The IEEE 6 bus system has the largest voltage drop on bus 3 by 3%, 2.9% and 2.2% for each phase a, b and c. The biggest power losses of the IEEE 6 bus system occur in the connecting line between bus 1 with bus 2 which is 32.5 KW, 28.1 KW and 37.1 KW, with total losses of 254.1 KW and 778.5 KVAR.

Cost Reduction in Clustering Based Unit Commitment Employing Hybrid Genetic-Simulated Annealing Technique

Fuel cost savings can be obtained by proper commitment of available generating units. This paper describes a new approach to the unit commitment problem through classification of units into various clusters based on hybrid technique of genetic algorithm and simulated annealing. This classification is carried out in order to reduce the overall operating cost and to satisfy the minimum up/down constraints easily. Unit commitment problem is an important optimizing task in daily operational planning of power systems which can be mathematically formulated as a large scale nonlinear mixed-integer minimization problem. A new methodology employing the concept of cluster algorithm called as additive and divisive hierarchical clustering has been employed based on hybrid technique of genetic algorithm and simulated annealing in order to carry out the technique of unit commitment. Proposed methodology involves two individual algorithms. While the load is increasing, additive cluster algorithm has been employed while divisive cluster algorithm is used when the load is decreasing. The technique that has been developed has been tested on system with generating units in range of 10–100 and the superior performance of the technique has been reported through simulation results.

Optimal Power Flow Subject to Non‐Gaussian Stochastic Uncertainties: An L2‐based Approach

AbstractThe optimal power flow (OPF) problem—i.e., the task to minimize power system operation costs while maintaining technical and network limitations—is key for the operational planning of power systems. The influx of inherently volatile renewable energy sources calls for methods that allow to consider stochasticity directly in the OPF problem. Modeling uncertainties as second‐order continuous random variables, the OPF problem subject to stochastic uncertainties can be posed as an infinite‐dimensional L2‐problem. A tractable and exact reformulation thereof can be obtained using polynomial chaos expansion, under mild assumptions. Polynomial chaos as such is a Hilbert space series expansion for random variables that is frequently employed for uncertainty propagation and uncertainty quantification. Polynomial chaos offers several advantages for OPF subject to stochastic uncertainties. For example, multivariate non‐Gaussian uncertainties can be considered straightforwardly. Also, the solutions from polynomial chaos are effectively feedback laws in terms of the realizations of the uncertainty that are determined in a single numerical run.

Binary grey wolf optimizer models for profit based unit commitment of price-taking GENCO in electricity market

Abstract The restructuration of electric power sector has renovated the power system operational planning. In the deregulated market, electricity is treated as an entity unlike in the traditional vertically integrated market model where it is treated as a service provided by the generation companies (GENCOs). The task of GENCO is to perform self-scheduling of available units so as to achieve maximum profit in restructured power sector. Therefore, the problem of profit based unit commitment (PBUC) in deregulated markets can be formulated as a commitment and generation allocation through self-scheduling procedure. The commitment schedule optimization, i.e whether the status of a thermal unit is to be changed to on or off state, is a binary problem. Thus, the PBUC problem requires binary optimization for commitment and real valued optimization for generation allocation. In this paper, three binary grey wolf optimizer (BGWO) models are presented to solve the profit based self-scheduling problem of GENCO. The BGWO models proposed in this paper differ with respect to the transformation function used to map real valued wolf position into a binary variable. The binary mapping of commitment status is carried out using sigmoid and tangent hyperbolic transfer functions. Also, in the sigmoidal transfer function, two binary transformation procedures, namely crossover and conventional sigmoidal transfer function, are presented. The effectiveness of the proposed models in improving the solution quality of PBUC problem is examined using two test systems, a 3 unit and a 10 unit test system. In addition, two cases of GENCO market participation with and without reserve market participation are simulated. In the test case with reserve market participation, two commonly used reserve payment models are examined. Simulation results are presented and compared to existing approaches that have been used to solve the PBUC problem. The simulation results and statistical analysis demonstrate the improved solution quality (profit or fitness value) of the PBUC problem and statistical significance of the BGWO models with respect to solution quality obtained as compared to other established meta-heuristic approaches.

State-of-the-Art Impacts of Smart Grid in the Power Systems Operation and Expansion Planning

In this article, a state-of-the-art review on the impacts of Smart Grid in the operation and planning of the expansion of the Electric Power System is presented. The concept of Smart Grid is increasingly integrated in conventional networks, and the impact that this new technology generates on operation and planning must be investigated. Thus, in this article a survey of the main scientific publications was made with the purpose of determining the impacts and methodologies most used in this analysis. Efforts to analyze impacts are large because of the high degree of uncertainty of this new technology added to the problem. After the bibliographic survey, it was concluded that Robust Optimization and Genetic Algorithms are methodologies that are effective in problems of this nature and are the most adopted methods.

Optimal power flow control in the system with offshore wind power plants connected to the MTDC network

The construction of offshore wind power plants usually requires transmission of the large amount of energy over the large distances with cable power lines. The most effective solution for the connection of large number of remote offshore wind power plants to the rest of the system is with MTDC transmission network. In this paper, the methodology for optimal control of power converters for minimizing power losses in the system is presented. The main task of the proposed methodology is to determine, based on the forecasted production of wind power plants, the performance characteristics of the power converters that would enable their optimal decentralized work in the presence of intermittent production from wind power plants. The determination of the power converter characteristics is conducted during day-ahead power system operation planning, based on which the power converters, during a real time control, maintains optimal operation of the system locally, without communication with each another. The proposed methodology is tested on the example of MTDC network with 5 buses connected to the IEEE 14-bus test system.

Quantifying the importance of power system operation constraints in power system planning models: A case study for electricity storage

The realized and expected growth of variable renewable energy sources challenges both power system operation and power system planning. A decreasing share of dispatchable generation technologies in electricity generation and an increasing need for short-term flexibility means that the added value of alternative short-term flexibility providers, such as electricity storage, becomes important. This paper studies the way in which alternative flexible resources, here storage technologies, generate added value by running various set-ups of a power system planning model with different levels of operational detail. Firstly, this allows to identify the different roles flexible technologies play in power system operation, and the subsequent impact on power system planning. Secondly, this allows to determine the importance of operational detail in power system planning models to accurately assess the value of flexibility. Results show that storage technologies have the technical ability to provide firm capacity, smoothen the residual load curve, manage the impact of hourly dispatch constraints and provide reserve capacity. Nevertheless, the extent to which this is translated into investment depends on how cost-effectively a technology can offer these services. Results further show that to fully capture the added value of flexibility providers high operational detail is needed in planning models.

Security Constrained Unit Commitment (SCUC) formulation and its solving with Modified Imperialist Competitive Algorithm (MICA)

One of the most important optimization problems in operation planning of power systems is unit commitment (UC). Consideration of transmission lines and operation constraints in UC problem leads to a more general problem known as Security Constrained Unit Commitment (SCUC). The SCUC can be formulated as a very large scale mixed-integer problem in practical utility grids. Solving SCUC can take tremendous time due to its huge dimension. In this paper, a formulation of SCUC problem, considering practical constraints and nonlinear characteristics including bus voltage limits, line flow limits, in addition to prevailing constraints such as hourly power demand, system spinning reserves, ramp up and down limits, minimum up and down time (MUT/MDT) limits, and emission limits, is presented. The Modified Imperialistic Competitive Algorithm (MICA), using a priority list (PL) for defining the initial state, is used to solve the mentioned optimization problem. The effectiveness of MICA method to solve the SCUC problem is shown on IEEE 30-bus and 118-bus test systems and is compared with the application of some heuristic methods i.e. genetic algorithm (GA), particle swarm optimization (PSO) and other mathematical approaches i.e. mixed integer programming (MIP).

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.

An N-k Analytic Method of Composite Generation and Transmission with Interval Load

N-k contingency estimation plays a very important role in the operation and expansion planning of power systems, the method of which is traditionally based on heuristic screening. This paper stringently analyzes the best and worst states of power systems given the uncertainties of N-k contingency and interval load. For the sake of simplification and tractable computation, an approximate direct current (DC) power flow model was used. Rigorous optimization models were established for identifying the worst and best scenarios considering the contingencies of generators and transmission lines together with their uncertain loads. It is very useful to identify the worst N-k contingencies with interval loads. If the worst existing scenario meets security standards, all scenarios must satisfy it. The mathematical model established for finding the worst N-k contingency with interval load is a bi-level optimization model. In this paper, strong duality theory and mathematical linearization were applied to the solution of bi-level optimization. The computational results of standard cases validate the effectiveness of the proposed method and illustrate that generator contingency has more impact on minimum load shedding than transmission line contingency.

Grid Connected Wind Energy Conversion System for Peak Load Sharing Using Fuzzy Logic Controller

The rapidly growing renewable energy resources in context of smart grid environment entails the power system operational planning, optimal allocation, sizing and robust control of Distributed Generators (DGs). Generally, DGs include power generation from wind, solar, tidal, diesel or biogas etc. The wind energy systems are getting an immense attention of researchers in the recent decade. The conventional wind turbine is coupled with electrical generator through gear box. An efficient blade pitch control mechanism regulates the shaft speed of wind turbine and thus frequency of electrical generator against load variations and uncertain wind blows. This blade pitch control mechanism may be referred as Pitch Frequency Control (PFC) of wind energy system. In this research paper, a technique has been proposed for peak load sharing of utility supply using fuzzy logic based technique. The proposed technique has been modelled and simulated in Simulink environment of MATLAB. The simulation results show the improved performance for PFC of wind energy system. Moreover, the minimum percent overshoot and settling time back the simulation results. Thus the proposed technique reflects its significance by its application for wind energy systems consisting of low speed electrical generators.