Real Power Balance Research Articles

The design of an improved index system for frequency control in China Southern Power Grid

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode.Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line.Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets.

Dandelion Optimizer-Based Combined Automatic Voltage Regulation and Load Frequency Control in a Multi-Area, Multi-Source Interconnected Power System with Nonlinearities

Frequency, voltage, and power flow between different control zones in an interconnected power system are used to determine the standard quality of power. Therefore, the voltage and frequency control in an IPS is of vital importance to maintaining real and reactive power balance under varying load conditions. In this paper, a dandelion optimizer (DO)-based proportional-integral-proportional-derivative (PI-PD) controller is investigated for a realistic multi-area, multi-source, realistic IPS with nonlinearities. The output responses of the DO-based PI-PD were compared with the hybrid approach using artificial electric field-based fuzzy PID algorithm (HAEFA), Archimedes optimization algorithm (AOA)-based PI-PD, learning performance-based behavior optimization (LPBO)-based PI-PD and modified particle swarm optimization (MPSO)-based PI-PD control schemes in a two-area network with 10% step load perturbation (SLP). The proposed strategy was also investigated in a two- and three-area IPS in the presence of different nonlinearities and SLPs. The simulation results and the comprehensive comparison between the different control schemes clearly confirm that the proposed DO-based PI-PD is very effective for realistic, multi-area multi-source IPS with nonlinearities.

Clustering Nepalese power system into stable islands considering real power balance

With the increasing demands and consecutively the size and complexity of the power system, blackouts have become unavoidable in today's power system. So, the study about plans and strategies for grid restoration after a blackout has also become a major necessity. This paper uses the strategy which first partitions the blackout area into several islands and restores them paralleled to accelerate the restoration task. An agglomerative clustering algorithm has been used to partition the network which solves a constrained optimization to maximize the generation load difference and form the clusters based on it. Two cases have been studied upon in the Integrated Nepalese Power System (INPS). Case I has taken two black start generators as partitioned the system two clusters whose generation load difference was found to be 200.308 MW and 321.778 MW respectively. Similarly, case II has taken three black start generators and partitioned the system into three islands whose generation load difference was found to be 142.632 MW, 313.912 MW, and 118.788 MW. These positive values indicate that the islands can be operated independently before synchronization even if the loads deviate from the forecasted value.

New metaheuristic methodology for solving security constrained hydrothermal unit commitment based on adaptive genetic algorithm

In this paper, a new metaheuristic technique based on genetic algorithm (GA), adaptive strategy and two constraint handling repair mechanisms is proposed for solving the Security Constrained Hydrothermal Unit Commitment (SCHTUC). The performance efficiency of the proposed technique is demonstrated on IEEE 30 BUS test system consisting of four thermal units and two hydro power plants. A wide range of thermal, hydraulic, and security constraints such as real power balance constraint, ramp rate, minimum up/down time, minimum and maximum limits of thermal and hydro units, prohibited operating zones (POZ), valve point effect, spinning reserve, available production, water dynamic balance and transmission line constraint are taken into account. The simulation results obtained from the new binary/real GA are compared with the outcomes obtained from some heuristic methods, such as DA-PSO and PSO-GWO to reveal the validity and verify the feasibility of the proposed technique. The test results show that the proposed metaheuristic technique has the capability of obtaining better solutions with respect to other optimization methods which are implemented on this optimization problem.

Optimization of Distributed Solar Photovoltaic Power Generation in Day-ahead Electricity Market Incorporating Irradiance Uncertainty

This paper proposes a simple and practical approach to model the uncertainty of solar irradiance and determines the optimized day-ahead (DA) schedule of electricity market. The problem formulation incorporates the power output of distributed solar photovoltaic generator (DSPVG) and forecasted load demands with a specified level of certainty. The proposed approach determines the certainty levels of the random variables (solar irradiance and forecasted load demand) from their probability density function curves. In this process of optimization, the energy storage system (ESS) has also been modeled based on the fact that the energy stored during low locational marginal price (LMP) periods and dispatched during high LMP periods would strengthen the economy of DA schedule. The objective of the formulated non-linear optimization problem is to maximize the social welfare of market participants, which incorporates the assured generation outputs of DSPVG, subject to real and reactive power balance and transmission capability constraints of the system and charging/dis-charging and energy storage constraints of ESS. The simulation has been performed on the Indian utility 62-bus system. The results are presented with a large number of cases to demonstrate the effectiveness of the proposed approach for the efficient, economic and reliable operation of DA electricity markets.

Turbo-Expander Generators for Supplemental Power Generation in LNG Liquefaction Plants

Incentives to increase efficiency and reduce CO2 emissions have led to the development of many plant process designs incorporating turbo-expanders. Turbo-expanders provide the most efficient solution when it is required to reduce the pressure of a fluid stream. By expanding high pressure fluid, energy in the high-pressure fluid entering the turbo-expander can be efficiently used for electrical power generation. The energy recovered from the expansion is supplementary, thereby improving the thermal efficiency of the plant without leaving any noticeable trace of greenhouse gas emissions. This article focuses on both mechanical and electrical considerations for the implementation of turbo-expander generators. In an example of LNG liquefaction plant, the article discusses the plant thermal efficiency, turbo-expander performance curve, generator sizing, and the choice between the induction and the synchronous generator. The impact on the plant real and reactive power balance and the system stability is shown in an example. Furthermore, this article evaluates the options for the turbo-expander generator tie-in points within the electrical system.

Optimal Design and Model Predictive Control of Standalone HRES: A Real Case Study for Residential Demand Side Management

Conventional electricity generation is one of the greatest sources of CO2 emissions. For a successful transformation of conventional energy systems into non-polluting and renewable energy systems, technology-focused traditional systems and economics must be combined for a more accurate holistic viewpoint with consideration of socio-political, technical, economic and environmental factors. Hybrid energy systems are considered the most feasible solution to the stochastic nature of renewable energy resources (RERs). Different renewable sources such as wind, solar, and hydrogen fuel cells can be integrated to form hybrid systems. An energy management strategy (EMS) is a strategy for power flow coordination among different components, by considering power demand and other constraints. The choice for an accurate EMS is the key element of a hybrid system as it is instrumental in providing an optimum solution of the hybrid system design and operation management. The objective of the optimization is to find suitable configurations for cost-effective solutions. Optimization and EMS must be treated as one entity to completely understand the system design. This study focuses on a techno-economic analysis with an optimized sizing of a hybrid renewable energy system (HRES) components to meet the residential load demand of a specific area in Pakistan. Nine different scenarios based on the PV-wind-diesel-BSS-converter system are investigated in terms of total net present cost (TNPC), Levelized cost of energy (LCOE), and greenhouse gas (GHG) emissions to find the optimal system design. HOMER Pro software is used to develop the HRES model and for simulation analysis, with optimal sizing of each component for an economical solution. Simulation studies established that PV-wind-BSS-converter is the best suitable choice for the given location, and the optimal component sizes were determined. The TNPC of this system is $47,398 and the LCOE is $ 0.309/kWh. This represents an 81.7 % decrease in overall cost, compared to the base case (diesel only) and a 100% reduction in harmful gases while satisfying 100 % of the energy requirement with a 63.9 % of the surplus. MATLAB/Simulink model is developed for the optimum HRES system design. Its validity is tested by maintaining bus voltages (dc and ac), the secure operation range of storage SOC and real power balance among different components of the hybrid renewable energy system (HRES), and an effective ac voltage, irrespective of external perturbations. Model predictive control (MPC) is regarded as a high-performing algorithm. Since power converters are largely applied in microgrids (MGs), the problem formulation with MPC for a reconfigurable bidirectional voltage source converter (VSC) is applied in this work for hybrid MG. The inevitable fluctuations due to the linear and non-linear loads and the nature of renewable sources are addressed. The regulation of ac voltage is implemented through a finite control set model predictive control (FCS-MPC) based active front end (AFE) rectifier, while direct power MPC (DPMPC) is used to control the power during grid-connected operation. The regulation of an ac load voltage is done through voltage based MPC (MPVC) in the islanding operation of the MG. Moreover, the HRES transition from grid-tied to grid-isolated mode is comprehensively analyzed. MATLAB/Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> software certified the robustness and evaluated the performance of the proposed HRES model under different varying loads viz. balanced, unbalanced, and nonlinear. The proposed strategy offers superior performance with low total harmonic distortion (THD), compared to previously developed strategies. The output waveform of voltage and current have THD of 0.28 % compared to 3.71 % with the conventional strategy. The contributions of this paper lie in the sequential use of HOMER as well as MATLAB tools and in the validation of the suggested HRES plan for the considered location; along with the implementation of FCS-MPC for a reconfigurable bidirectional VSC.

Politeness, gender and the social balance of the Homeric household

AbstractThis paper focusses on the role of women within the Homeric household (οἶκος, “oikos”) as related to politeness. The social balance of the household has its fulcrum in the relation between the householder and his wife, and the latter has a crucial role in preserving the face of her husband and hence his authority in the oikos. In practice, to preserve his public image within the oikos, householders delegate a core part of their authority to their wives, and in exchange of this wife-characters such as Penelope or the goddess Hera are keen always to stage the subaltern role, which women have in the Homeric society. The paper compares specific examples of similar politeness strategies to the behaviour of Helen in Book 6 of theIliad(321–356). Helen enacts a reverse politeness strategy aiming to make her husband Paris’s face collapse in front of Hector. By combining Erving Goffman’s concepts of “face” and “social situation” and the Homeric values of τιμή (“timē”) and αἰδώς (“aidōs”) into a framework for studying politeness in the epics, it becomes possible to shed light on the real power balance that – underneath the veil of politeness – characterises the relationship between the householder and his wife in the Homeric oikos.

Optimal Power Flow Incorporating Frequency Security Constraint

Optimal power flow (OPF) minimizes the production cost, while satisfying the constraints on the system, such as real and reactive power balance, equipment capability constraints, and voltage limits. This article presents a method of incorporating a frequency security constraint within the framework of the OPF problem. Increasing displacement of conventional generation by renewable resources that contribute little or no frequency regulation capability may necessitate the enforcement of such a constraint to meet frequency security requirements. The system frequency is required to be maintained within a safe limit, thus indicating the balance between generation and consumption. Hence, the solution for optimization of power flow should not only present a minimum cost of generation within the operating conditions, but also ensure frequency stability. In order to obtain this solution, the requirement of frequency stability is introduced as a new constraint of the power dispatch problem and is represented by the maximum frequency deviation limit. This new constraint is constructed as a nonlinear function of system inertia and the frequency regulation constant, since frequency deviation is highly sensitive to these factors. It is shown that the inclusion of this constraint causes the OPF to preferentially select higher inertia generators as necessary, to satisfy the frequency security requirement. A genetic algorithm is utilized as the optimization tool in this article. The IEEE RTS-79 test system is used to demonstrate efficacy of the proposed method.

Finding Optimal Load Dispatch Solutions by Using a Proposed Cuckoo Search Algorithm

Optimal load dispatch (OLD) is an important engineering problem in power system optimization field due to its significance of reducing the amount of electric generation fuel and increasing benefit. In the paper, an improved cuckoo search algorithm (ICSA) is proposed for determining optimal generation of all available thermal generation units so that all constraints consisting of prohibited power zone (PPZ), real power balance (RPB), power generation limitations (PGL), ramp rate limits (RRL), and real power reserve (RPR) are completely satisfied. The proposed ICSA method performance is more robust than conventional Cuckoo search algorithm (CCSA) by applying new modifications. Compared to CCSA, the proposed ICSA approach can obtain high quality solutions and speed up the solution search ability. The ICSA robustness is verified on different systems with diversification of objective functions as well as the considered constraint set. The results from the proposed ICSA method are compared to other algorithms for comparison. The result comparison analysis indicates that the proposed ICSA approach is more robust than CCSA and other existing optimization approaches in finding solutions with significant quality and shortening simulation time. Consequently, it should lead to a conclusion that the proposed ICSA approach deserves to be applied for finding solutions of OLD problem in power system optimization field.

Gumbel copula based multi interval ramp product for power system flexibility enhancement

High renewable penetration in modern power systems increases uncertainty and variability of net load, causing frequent real time system imbalances. This necessitates increased generation flexibility to mitigate such imbalances. Regulation services and transfer tie lines enhance system flexibility, but limited availability of conventional generating units with lower operational time frame and the associated high investment reduces their applicability. This requires development of flexible ramp products (FRP) to maintain real time power balance. FRP design should consider both variability and uncertainty of net load, with accurate uncertainty modeling. Existing ramp product design uses approximate net load uncertainty, which reduces their effectiveness. Further, existing FRP design primarily focus on wind dominated systems. Rapidly increasing solar generation from utility level plants show higher variability than wind in very short time frames. Rapid coherent changes from such plants cause massive generation variation. Considering very short term uncertainty and variability of utility level solar plants, this paper proposes a novel multi-interval FRP design based on net load forecasts. Proposed FRP design considers both start and end time uncertainty of each time interval, compared to existing end time uncertainty based designs. To enhance the net load uncertainty estimation accuracy, proposed model uses Gumbel copula function based joint probability distribution (JPD) of load, solar and wind forecasting errors, because it covers all extreme forecasting errors due to max-stable property. Results show that proposed design effectively manages the variability and uncertainty of net load, offering significant cost savings.

SimPower-based analysis and design of a hybrid wind–diesel-superconducting magnetic energy storage system for simultaneous frequency and voltage control

Power quality control in a stand-alone power system is a demanding task. For satisfactory operation, such systems are being augmented with fast-acting energy storage devices. In this article, a stand-alone wind–diesel system augmented with a small-rating superconducting magnetic energy storage system is considered for both reactive and real power balance. Suitable controllers are proposed which force the superconducting magnetic energy storage system to exchange both reactive and real power with the system under various perturbations. A simulation platform is developed in SimPower to virtually validate the system model and control design aspects. Superconducting magnetic energy storage system and its power electronic interface are represented by average value models and the various controller parameters are tuned using Genetic Algorithm. Simulation results show that both voltage and frequency of the system are improved.

Transactive-Market-Based Operation of Distributed Electrical Energy Storage with Grid Constraints

In a transactive energy market, distributed energy resources (DERs) such as dispatchable distributed generators (DGs), electrical energy storages (EESs), distribution-scale load aggregators (LAs), and renewable energy sources (RESs) have to earn their share of supply or demand through a bidding process. In such a market, the distribution system operator (DSO) may optimally schedule these resources, first in a forward market, i.e., day-ahead, and in a real-time market later on, while maintaining a reliable and economic distribution grid. In this paper, an efficient day-ahead scheduling of these resources, in the presence of interaction with wholesale market at the locational marginal price (LMP), is studied. Due to inclusion of EES units with integer constraints, a detailed mixed integer linear programming (MILP) formulation that incorporates simplified DistFlow equations to account for grid constraints is proposed. Convex quadratic line and transformer apparent power flow constraints have been linearized using an outer approximation. The proposed model schedules DERs based on distribution locational marginal price (DLMP), which is obtained as the Lagrange multiplier of the real power balance constraint at each distribution bus while maintaining physical grid constraints such as line limits, transformer limits, and bus voltage magnitudes. Case studies are performed on a modified IEEE 13-bus system with high DER penetration. Simulation results show the validity and efficiency of the proposed model.

Adaptive partitioning approach to self‐sustained smart grid

Effective network partitioning becomes an essential step to realise self-sustained smart grid, which serves as a prerequisite for ‘self-healing’ enabled decentralised control. Splitting the power network (PN) into areas is the last resort to avoid the spread of disruption and to maintain as many network survivals as possible. This study aims to resolve the issue of multi-objective PN partitioning by deploying a newly proposed hybrid approach concerning both real power balance and voltage profile. The proposed approach combines the Laplacian spectrum and self-organising map, which adaptively attains self-sustained network partitions on different operating conditions. The resultant partitions are characterised by the minimal intra-area real power imbalance with a healthy voltage profile. The authors experimentally evaluate the partitioning effectiveness and computational efficiency in several case studies including on the New England 39-bus, IEEE 118-bus, and Polish 2383-bus transmission systems.

Operation of small hybrid autonomous power generation system in isolated, interconnected and grid connected modes

In this paper, the autonomous hybrid power system consisting of wind-turbine generator (WTG), diesel-engine generator (DEG), fuel-cell (FC) and aqua-electrolyzer (AE) is studied. DEG acts as slack generator, whose output power is controlled to have real power balance in the system to keep the frequency deviation within the limit. This paper proposes a new control scheme incorporating PID and PD controllers, to control the output power of DEG to keep real power-frequency balance in the system. PID controller is introduced to make the frequency deviation zero under steady-state, after any disturbance. PD controller is incorporated to damp out the oscillations in system frequency, after disturbance. Detail analysis of the effect of PID and PD controllers on system mode is presented using participation factor. The sensitivity of system modes to the variation of controller parameters is investigated. Simulated responses under isolated operation are presented to show that the proposed control strategy is capable of maintaining power-frequency balance in the system, following any disturbance. The mathematical model of the interconnected system is developed. One feedback loop is added to the existing PID controller in each area to reduce the tie-line power loss. Grid-connected and distribution system connected operation of the hybrid power system are also investigated.

Pseudo-gradient Based Particle Swarm Optimization with Constriction Factor for Multi Objective Optimal Power Flow

This paper proposes a pseudo-gradient based particle swarm optimization with constriction factor (PG-PSOCF) method for solving multiobjective optimal power flow (MOOPF) problem. The proposed PG-PSOCF is the conventional particle swarm optimization based on constriction factor based on pseudo gradient to enhance its search ability for optimization problems. The proposed method is to deal with the MOOPF problem by minimizing the total cost and emission from generators while satisfying various constraints of real and reactive power balance, real and reactive power limits, bus voltage limits, shunt capacitor limits and transmission limits. Test results on the IEEE 30-bus system have indicated that the proposed method is more efficient than many other methods in the literature. Therefore, the proposed PG-PSOCF can be an effectively alternative method for solving the MOOPF problem.

Energy Reserve Trade Optimization for Wind Generators Using Black and Scholes Options in Small-Size Power Systems

Tremendous attention has been paid to wind energy. This renewable source of clean energy has become very appealing to investors, engineers, and environmentalists. From the power system operations perspective, the uncertain nature of wind power poses serious challenges. To reduce the effect of wind power uncertainty, a reserve procurement methodology using financial derivative such as “Options” is very promising. This paper proposes a new optimization model to procure secondary reserves to overcome wind power uncertainty by using Black and Scholes financial options while estimating short-term wind energy forecast errors by using the Gaussian distribution function. The proposed model integrates power system security constraints. At the optimal solution, the lambda multiplier associated with real power balance equations provides option premiums and strike prices.

A novel air-conditioning system for proactive power demand response to smart grid

Power demand response is considered as one of the most promising solutions in relieving the power imbalance of an electrical grid that results a series of critical problems to the gird and end-users. In order to effectively make use of the demand response potentials of buildings, this paper presents a novel air-conditioning system with proactive demand control for daily load shifting and real time power balance in the developing smart grid. This system consists of a chilled water storage system (CWS) and a temperature and humidity independent control (THIC) air-conditioning system, which can significantly reduce the storage volume of the chilled water tank and effectively enable a building with more flexibility in changing its electricity usage patterns. The power demand of the proposed air-conditioning system can be flexibly controlled as desired by implementing two types of demand response strategies: demand side bidding (DSB) strategy and demand as frequency controlled reserve (DFR) strategy, in respond to the day-ahead and hour-ahead power change requirements of the grid, respectively. Considerable benefits (e.g., energy and cost savings) can be achieved for both the electricity utilities and building owners under incentive pricing or tariffs. A case study is conducted in a simulation platform to demonstrate the application of the proposed system in an office building.

Optimal Unit Commitment with Ant Colony Algorithm

This paper presents an improved ant colony search algorithm that is suitable for solving unit commitment (UC) problems. Ant colony algorithm (ACA) is a meta-heuristic technique for solving hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback, distributed computation as well as constructive greedy heuristic. The ACA was inspired by the behavior of real ants that are capable of finding the shortest path from food sources to the nest without using visual cues. The constraints used in the solution of the UC problem using this approach are: real power balance, real power operating limits of generating units, spinning reserve, startup cost, and minimum up and down time constraints. The approach determines the units schedule followed by the consideration of unit transition related constraints. The proposed approach is expected to yield a better operational cost for the UC problem of production of 50 power plant units. UC problem limits the application of traditional optimization techniques to small size system. Recently, meta-heuristic approaches became popular in the effort to overcome shortcomings of traditional optimization techniques. Techniques such as genetic algorithms, evolutionary programming, simulated annealing, and tabu search have been widely investigated to solve the UC problem. These methods can accommodate more complicated constraints and are claimed to produce solutions of improved quality. The ACA is a meta-heuristic technique proposed to find a near optimal solution to the UC problem. The ACA was inspired by the behavior of real ants which are capable of finding the shortest path from food sources to the nest without using visual cues. The technique has been tested successfully on diverse complex combinatorial optimization problems. Also, it has been shown that the ant colony method performs with little variability over problem diversity or random number seed. In addition, the ACA introduces an entirely new solution at each iteration, while other meta-heuristic optimization techniques are based on improvement of the solution or set of solutions obtained from the previous iteration. During the solution process of the UC problem using the ACA, the obtained solution is always feasible. Moreover, the ACA does not require major assumptions and approximations that limit the solution space.

Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.