Power System Operational Constraints Research Articles

Dynamic investment planning of CVR implementation considering PEVs’ reactive power compensation capability

AbstractConservation voltage reduction (CVR) is a strategy that tries to save energy consumption by managing consumers’ voltage. On the other hand, enhancing reactive power compensation in the power system gives rise to CVR implementation more effectively. This paper addresses how the power system planner could encourage investment to establish the above‐mentioned topics. Here, a model for encouraging industrial loads to dynamically invest in implementing the CVR strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. Industrial loads benefit from saving in their electricity bills and incentive payments that might be paid by utilities (if necessary). The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. The proposed planning problem is modelled as a non‐linear optimization problem that can be solved by off‐the‐shelf software, for example, GAMS. The proposed model is applied to the Ontario transmission system. The numerical results show the proposed model's effectiveness in designing an investment plan for industrial loads to implement the CVR strategy and upgrade PEVs’ chargers.

Energy Storage Mix Optimization Based on Time Sequence Analysis Methodology for Surplus Renewable Energy Utilization

Increasing the proportion of carbon-free power sources, such as renewable energy, is essential for transitioning to a zero-carbon power system. However, when the rate of grid expansion and flexibility cannot match the rate of renewable energy increase, surplus energy is the result. Surplus energy can be discarded through curtailment or stored and utilized when required. The optimal equipment configuration of the storage system should be determined based on the surplus energy characteristics. This study proposes an optimal energy storage mix configuration method by considering long-term forecasts of surplus energy in the South Korean renewable energy supply and power grid expansion plan. The surplus energy by time slot is comprehensively analyzed considering renewable energy power output, power demand, and power system operation constraints. We calculate the required power and energy of storage devices. Furthermore, we construct a long-term optimal energy storage mix using surplus energy generation patterns and technical and economical characteristics of storage technologies. The total cost minimization was considered as the objective function, comprising three elements: initial construction, equipment replacement, and loss costs for charging and discharging. We propose a time sequence analysis (TSA) method that enables chronological analysis from the starting year to the final target year. The TSA method provides an energy storage mix configuration roadmap that can utilize surplus energy for various years over the entire period, considering the annual increase in surplus energy and commercialization timing of each storage technology. We compare the difference between our proposed TSA method and the method that analyzes only the final target year to validate the superiority of this methodology.

A Mid/Long-Term Optimization Model of Power System Considering Cross-Regional Power Trade and Renewable Energy Absorption Interval

With the integration of large-scale renewable energy into the power grids, cross-regional power trade can play a major role in promoting renewable energy consumption, as it can effectively achieve the optimal allocation of interconnected power grid resources and ensure the safe and economic operation of the power grid. An optimization model on a mid/long-term scale is established, considering the relationship between the renewable energy absorption interval and the regulation of resources in the system. The model is based on the load block curve and the renewable energy power model, considering the maintenance constraints of conventional units, the operation constraints of conventional units and renewable energy units, cross-regional power trade constraints and system operation constraints. By analyzing the results of the adapted IEEE RELIABILITY TEST SYSTEM (IEEE-RTS), the validity of the model and method proposed in this paper is proven. The results show that the coordinated optimization of conventional energy and renewable energy in the system can be achieved, and the complementarity of power supply and load can be promoted.

Implementation effects of economics and market operations based model for traditionally integrated power systems

<p><span id="docs-internal-guid-c7335217-7fff-17cb-2b2b-3da634576ee1"><span>The main objective of this paper is to introduce power system economic operations in traditionally integrated power systems and market operations in deregulated power systems and study its effects. The power system economic operation is mathematically treated as an optimization problem. Also, a function of economic operation is to minimize generation cost, transmission losses, and so on, subject to power system operation constraints. In this paper, we start from generation cost formulations and introduce traditional economic dispatch model, optimal power flow model, and unit commitment model. With the deregulation of the power industry, integrated power system is unbundled to generation, transmission, and distribution. Electricity is traded in the wholesale market. Small customers purchase energy from electricity retailers through the retail market. The electricity market is operated for energy trading while satisfying power system operation requirements. Electricity market is mathematically modelled as an optimization problem that is subject to power system operation constraints and market operation constraints.</span></span></p>

Fuzzy logic based load frequency control of power system

Power system is a large interconnected and complex network with several sources and loads. The rise in population leads to changes and uncertainty in load demand. It is the accountability of the utilities to meet the load demand without violating power system operational constraints. Load frequency control is solely responsible for supplying reliable electrical power to the consumers. Its ultimate function is to regulate the output power of the utility generator with respect to load change without affecting system frequency and also to control the tie-line power such as to sustain the frequency and power flow in further areas within recommended limits. The concept proposed in the paper represents the load frequency control of two area system with conventional proportional integral controller, proportional integral derivative controller, fuzzy logic controller and fuzzy tuned proportional integral controller. Simulation studies are carried out using SIMULINK.

Cost-Effective Outage Management in Smart Grid under Single, Multiple, and Critical Fault Conditions through Teaching–Learning Algorithm

A distribution system becomes the most essential part of a power system as it links the utility and utility customers. Under abnormal conditions of the system, a definitive goal of the utility is to provide continuous power supply to the customers. This demands a fast restoration process and provision of optimal solutions without violating the power system operational constraints. The main objective of the proposed work is to reduce the service restoration cost (SRC) along with the elimination of the out-of-service loads. In addition, this work concentrates on the minimal usage and finding of optimal locations for additional equipment, such as capacitor placement (CP) and distributed generators (DGs). This paper proposes a two-stage strategy, namely, the service restoration phase and optimization phase. The first phase ensures the restoration of the system from the fault condition, and the second phase identifies the optimal solution with reconfiguration, CP, and DG placement. The optimization phase uses the teaching–learning algorithm (TLA) for optimal restructuring and optimal capacitor and DG placement. The robustness of the algorithm is validated by addressing the test cases under different fault conditions, such as single, multiple, and critical. The effectiveness of the proposed strategy is exhibited with the implementation to IEEE 33-bus radial distribution system (RDS) and 83-bus Taiwan Power Distribution Company (TPDC) System.

Market-oriented transmission expansion planning using non-linear programming and multi-criteria data envelopment analysis

Abstract This paper presents a novel and effective methodology for transmission expansion planning (TEP) in a double-auction based electricity market. The TEP problem has been formulated as a nonlinear programming problem to maximize the social welfare function of market participants (Generation and Distribution Companies), subject to power system operational and security constraints. The proposed methodology has been solved in two parts: in the first part, nonlinear TEP problem has been solved for a number of market scenarios to identify a set of prospective transmission lines; and in second part, multi-criteria data envelopment analysis has been applied on the identified set of prospective lines to select the most effective ones for transmission reinforcement. The stopping criterion of the TEP methodology has been applied based on the fact that increment in social welfare obtained by transmission reinforcement should be more than the investment cost of Transmission Company. The proposed methodology is well suited for nonlinear modeling of power systems as it reduces the complexity and computational burden of TEP problem substantially. To illustrate the efficacy of the proposed methodology, simulation result of Garver’s six-bus, IEEE 24-bus reliability test, and IEEE 662-bus systems have been presented.

Scheduling distributed energy resources and smart buildings of a microgrid via multi‐time scale and model predictive control method

To schedule the distributed energy resources (DERs) and smart buildings of a microgrid in an optimal way and consider the uncertainties associated with forecasting data, a two-stage scheduling framework is proposed in this study. In stage I, a day-ahead dynamic optimal economic scheduling method is proposed to minimise the daily operating cost of the microgrid. In stage II, a model predictive control based intra-hour adjustment method is proposed to reschedule the DERs and smart buildings to cope with the uncertainties. A virtual energy storage system is modelled and scheduled as a flexible unit using the inertia of building in both stages. The underlying electric network and the associated power flow and system operational constraints of the microgrid are considered in the proposed scheduling method. Numerical studies demonstrate that the proposed method can reduce the daily operating cost in stage I and smooth the fluctuations of the electric tie-line power of the microgrid caused by the day-ahead forecasting errors in stage II. Meanwhile, the fluctuations of the electric tie-line power with the MPC based strategy are better smoothed compared with the traditional open-loop and single-period based optimisation methods, which demonstrates the better performance of the proposed scheduling method in a time-varying context.

Cost savings and emission reduction capability of wind-integrated power systems

In the face of energy crisis and environmental impacts of conventional energy sources, renewable energy sources have attracted tremendous interest. Among many resources, wind energy has proven to be a promising renewable energy option in recent times. The primary objective of utilizing renewable energy is its environmental sustainability and the idea of emission reduction constitutes a prominent part in it. The emission here refers to the CO2, SO2 and NOx emissions from thermal power plants. However, recent research works suggest that there remains a considerable anomaly between the claims of emission reduction by wind energy developers and the actual emissions, particularly with the integration of wind power into the grid system. The estimation methodology involving tools like wind resource assessment and energy capture may not provide accurate estimates due to some power system generation and operational constraints. The objective of the present study is to analyse the impact of wind turbine ratings and economic emission dispatch (EED) on the generation cost and emissions in wind integrated power systems. In this regard, the study investigates the concept of emission factor and wind-integrated EED. Wind turbines available at different rated power and rated speeds are incorporated in four different test power systems and the actual cost and emissions over a selected period are evaluated. Finally, the impact of the wind power integration on the generation cost savings and emission reduction is investigated.

Stacked Revenue and Technical Benefits of a Grid-Connected Energy Storage System

This paper proposes a comprehensive evaluation of stacked revenue generated from grid-connected energy storage systems (ESSs). The stacked revenue from an ESS cannot be calculated by merely aggregating the benefits from various applications (e.g., energy arbitrage, frequency regulation, and outage mitigation) as the ESS may not be available for all types of applications during the same time intervals. This is because a quantity committed to one market may not be committed to another. In this paper, different types of applications for grid-connected ESSs are identified, and a model incorporating component reliability, power system operation constraints, and storage system operation constraints is developed to evaluate the composite revenue generated from these applications. In this model, the types of applications of ESSs are prioritized according to their intended contributions and system operating conditions. Sequential Monte Carlo simulation is used for evaluating the reliability improvement and a quadratically constrained linear programing model is built for estimating the maximum revenue from arbitrage and regulation markets. The proposed method is demonstrated on the IEEE reliability test system using historical PJM price data.

Optimal Placement of GIC Blocking Devices Considering Equipment Thermal Limits and Power System Operation Constraints

A practical mitigation of the geomagnetic disturbance (GMD) consequences in power systems is not possible without taking into account the equipment thermal limits and operational constraints. This paper presents such limitations, develops the required equations and characteristics, and proposes a general and comprehensive approach to incorporate these limits in any short-term or long-term geomagnetically induced current (GIC) mitigating solution. As an application, this study formulates the limits in the context of an optimal placement of the GIC blocking devices at the neutral point of the power transformers in the IEEE 118-bus benchmark study system. The proposed optimization problem takes into account synchronous generator real/reactive power capability, acceptable bus voltage magnitudes, transformer hot-spot heating, transmission-line thermal limits, capacitor bank harmonic loading, and synchronous generator rotor heating. This paper shows that the existing standards significantly underestimate the generator rotor heating under the GMD conditions and propose more accurate alternatives. The study results show that considering the equipment thermal limits results in a noticeably different solution when compared with the case of ignoring such limits.

Evaluating the CO2 emissions reduction potential and cost of power sector re-dispatch

Prior studies of the U.S. electricity sector have recognized the potential to reduce carbon dioxide (CO2) emissions by substituting generation from coal-fired units with generation from under-utilized and lower-emitting natural gas-fired units; in fact, this type of “re-dispatch” was invoked as one of the three building blocks used to set the emissions targets under the Environmental Protection Agency's Clean Power Plan. Despite the existence of surplus natural gas capacity in the U.S., power system operational constraints not often considered in power sector policy analyses, such as transmission congestion, generator ramping constraints, minimum generation constraints, planned and unplanned generator outages, and ancillary service requirements, could limit the potential and increase the cost of coal-to-gas re-dispatch. Using a highly detailed power system unit commitment and dispatch model, we estimate the maximum potential for re-dispatch in the Eastern Interconnection, which accounts for the majority of coal capacity and generation in the U.S. Under our reference assumptions, we find that maximizing coal-to-gas re-dispatch yields emissions reductions of 230 million metric tons (Mt), or 13% of power sector emissions in the Eastern Interconnection, with a corresponding average abatement cost of $15–$44 per metric ton of CO2, depending on the assumed supply elasticity of natural gas.

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.

Real-Time Energy Management in Microgrids

Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.

A Chance-constrained Optimization Model for Determining Renewables Penetration Limit in Power Systems

In this article, an optimization model determining renewables penetration limit in power systems is presented. The penetration limit is defined as the enabling renewables output with quantified maximum capacity avoiding the violation of power system operation constraints. Thus, an optimal power flow (OPF)-based model with chance constraints is built and a framework including a Monte Carlo-based genetic algorithm is designed. Moreover, a transient stability verification and correction strategy based on trajectory sensitivity is proposed and modularized in the extended framework. The feasibility of the proposed methodology is verified using several test scenarios, and some related factors are investigated as well. The results indicate that renewables penetration limit can be increased by improving those studied factors.

Distributed Optimal Energy Management in Microgrids

Energy management in microgrids is typically formulated as a nonlinear optimization problem. Solving it in a centralized manner does not only require high computational capabilities at the microgrid central controller (MGCC), but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all generations and loads are connected to one bus, and ignore the underlying power distribution network and the associated power flow and system operational constraints. Consequently, the schedules produced by those algorithms may violate those constraints and thus are not feasible in practice. Therefore, the focus of this paper is on the design of a distributed energy management strategy (EMS) for the optimal operation of microgrids with consideration of the distribution network and the associated constraints. Specifically, we formulate microgrid energy management as an optimal power flow problem, and propose a distributed EMS where the MGCC and the local controllers jointly compute an optimal schedule. We also provide an implementation of the proposed distributed EMS based on IEC 61850. As one demonstration, we apply the proposed distributed EMS to a real microgrid in Guangdong Province, China, consisting of photovoltaics, wind turbines, diesel generators, and a battery energy storage system. The simulation results demonstrate the effectiveness and fast convergence of the proposed distributed EMS.

Optimal allocation, sizing of PHEV parking lots in distribution system

Abstract The emerging of plug-in-hybrid electric vehicles (PHEV) results in the increase in the utilization of vehicles batteries for grid support. This paper presents a multi-objective algorithm to optimally determine the number of parking lots to be allocated in a distribution system. In addition, the algorithm optimally selects the locations and sizes of these parking lots. The proposed algorithms determine also the corresponding energy scheduling of the system resources. The objective of the proposed algorithm is to minimize the overall energy cost of the system. The problem is formulated as an optimization problem which is solved using artificial bee colony (ABC) and firefly algorithm taking into consideration the power system and PHEV operational constraints. The proposed algorithms are applied to a 33-bus radial distribution network. The test results indicate an improvement in the operational conditions of the system.

Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems

In this paper, a multi-objective framework is proposed for simultaneous optimal network reconfiguration and DG (distributed generation) power allocation. The proposed method encompasses objective functions of power losses, voltage stability, DG cost, and greenhouse gas emissions and it is optimized subject to power system operational and technical constraints. In order to solve the optimization problem, the HBB-BC (Hybrid Big Bang-Big Crunch) algorithm as one of the most recent heuristic tools is modified and employed here by introducing a mutation operator to enhance its exploration capability. To resolve the scaling problem of differently-scaled objective functions, a fuzzy membership is used to bring them into a same scale and then, the fuzzy fitness of the final objective function is utilized to measure the satisfaction level of the obtained solution. The proposed method is tested on balanced and unbalanced test systems and its results are comprehensively compared with previous methods considering different scenarios. According to results, the proposed method not only offers an enhanced exploration capability but also has a better converge rate compared with previous methods. In addition, the simultaneous network reconfiguration and DG power allocation leads to a more optimal result than separately doing tasks of reconfiguration and DG power allocation.

Optimal Residential Demand Response in Distribution Networks

Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply-demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by those works may end up with electricity consumption/shedding decisions that violate those constraints and thus are not feasible. In this paper, we study residential DR with consideration of the power distribution network and the associated constraints. We formulate residential DR as an optimal power flow problem and propose a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule. To complement our theoretical results, we also simulate an IEEE test distribution system. The simulation results demonstrate two interesting effects of DR. One is the location effect, meaning that the households far away from the feeder tend to reduce more demands in DR. The other is the rebound effect, meaning that DR may create a new peak after the DR event ends if the DR parameters are not chosen carefully. The two effects suggest certain rules we should follow when designing a DR program.

Intelligent energy management of optimally located renewable energy systems incorporating PHEV

The recent interest in plug-in-hybrid electric vehicles (PHEV) results in the increase in the utilization of vehicles batteries for grid support. In addition, the integration of renewable energy systems (RES) into electricity grid is a promising technique for addressing the environmental concerns. This paper presents a multi-objective algorithm to optimally allocate a number of renewable energy systems including parking lots for PHEV in a distribution system. The proposed algorithm determines the number, locations and sizes of the RES and parking lots. In addition, a rule based expert system is used to find the corresponding energy scheduling of the system resources. The objective of the proposed algorithm is to minimize the overall energy cost of the system. The problem is formulated as an optimization problem which is solved using artificial bee colony (ABC) algorithm taking into consideration the power system and PHEV operational constraints. The proposed algorithm is applied to a 45-bus distribution network of Alexandria, Egypt. The test results indicate an improvement in the operational conditions of the system.