Power Flow Limits Research Articles

Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework

Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.

A new comprehensive framework for cascading outage simulation in power systems

Simulating accurately and fast cascading outages play a critical role in power system security and resilience assessment. This paper proposes a new comprehensive framework for cascading outage simulation, articulated around an extended AC power flow. To address the key modelling features of cascading outage simulation, the proposed framework develops new algorithms for: a) detecting possible islanding, b) frequency control in each island: primary frequency control (PFC), under-frequency load shedding (UFLS) and over-frequency generator tripping (OFGT) and c) improved under-voltage load shedding (UVLS) and load models to avoid the divergence of AC power flow and allow handling voltage instability during the cascade. Tests on a realistic 60-bus system show that the proposed cascading outage simulator can successfully handle islanding, PFC, UFLS, OFGT, and UVLS; it simulates the cascade until there is no violation of system frequency, voltage, and power flow limits in islands that do not experience blackout.

Low-cost and flexible generation expansion planning with localization of power plants in a test bus system

ABSTRACT Generation Expansion Planning (GEP) is a challenge in electrical power systems because the size of the generating unit is large in scale, non-linear, long-term, and discontinuous. The existing GEP models use an array of methodological techniques. These models, however, primarily focus on the type of generation unit to be installed and when to be installed so as to reduce pollution and overall costs. They do not focus on the optimal location for installation. This research work proposes an IEEE bus-30 and IEEE bus-14 merged bus systems to fulfil the electrical load demand during the 5th and 10th years of planning. In stage 1, the GEP problem is resolved using Black Widow Optimization (BWO). In stage 2, the optimal location for generating units in the proposed bus system is resolved using a Chimp Optimization Algorithm (ChoA). The best location reduces the objective function (real power loss) and satisfies the voltage and power flow limits of the electrical power system. The performance of the proposed model is compared to that of existing optimization models. The results demonstrate that the proposed work reduces costs and provides flexible operations with reduced real power loss.

Research on high proportion of clean energy grid-connected oscillation risk prediction technology based on CNN and trend feature analysis

Oscillations, commonly known as a universal, propagative, and intricate event in the new power system, often give rise to generator tripping and load shedding, not only adversely affecting the power flow limit and the power angle stability but also posing threats to the lines of defense for stability and protection. Traditionally, emphasis has been laid on post-fault oscillation management, an emergency measure to deal with the impact and damage that have already affected the power grid. As such, this paper focuses on an oscillation prediction technique to detect oscillation energy early and intervene proactively to prevent further faults. This technique effectively lessens the damage caused by impacts and disconnects to the power grid. Firstly, this paper proposes the concept of disturbance power density and establishes the correlation between disturbance energy and the time domain, thereby exploring a method for evaluating the pattern of electrical quantities before power system oscillation. Secondly, it speeds up the time it takes to detect faults by catching nuances of voltage-current phase angle and impedance. Lastly, it puts forward a technique to cope with the intricacy and variety of power grid equipment using the convolutional neural network (CNN). This technique incorporates an integrated attention mechanism within a one-dimensional CNN model to capture the implicit mapping between voltage, active power, and reactive power at any time in the power system. This enables the model to self-learn multi-device characteristics and enhances the possibility of using theory in practical ways. Moreover, practical case studies also show that the prediction technique proposed in this paper can effectively issue warnings eight minutes before the occurrence of oscillation.

Coordinated Economic Operation of Hydrothermal Units with HVDC Link Based on Lagrange Multipliers

Coordinated operation of hydrothermal scheduling with HVDC links considering network constraints becomes a vital issue due to their remote location and recent induction in the existing power system. The nonlinear and complex nature of the problem introduces many variables and constraints which results in a heavy computational burden. A widespread approach for handling these complexities is to reformulate the problem by several linearization methods. In this paper, a Lagrange multipliers-based method is proposed for the solution of hydrothermal economic scheduling including HVDC link. This method solves equality constraint optimization problems. The linear programming approach is embedded with the Lagrange method to consider both equality and inequality constraints. The proposed technique has been used on piecewise linear variables and constraints of the system considering generation, water volume, and line power flow limits. The formulated method efficiently minimizes the operational cost of thermal units and maximizes the utilization of hydro units while meeting all generation, water volume, and the HVDC link constraints. The method was successfully implemented in two scenarios of a case study. In the first scenario, hydrothermal scheduling was performed on the typical network without an HVDC line limit and equal nodal prices were found with minimal thermal generation cost of $278,822.3. In the second scenario, the proposed method optimally dispatches units to meet the HVDC line limit and minimizes thermal generation cost to $279,025.4 while satisfying hydro, thermal, and other operating constraints. Both scenarios are implemented for a 24 h period. The results have been presented to illustrate the performance of the proposed method.

Smart distribution network operation based on energy management system considering economic-technical goals of network operator

This paper expresses economic flexible–securable operation (EFSO) in smart distribution networks (SDNs), including distributed generation and storage systems to produce green energy. This is structured as an optimization formulation to obtain a minimum expected operation cost, constrained by optimal power flow, environmental, security, and flexibility limits. Also, mathematical expressions of power sources and storage are presented. The scheme considers uncertainties related to load, price of energy, and generation power of renewable sources. The work adopts the point estimation method (PEM)-based stochastic optimization to model the uncertainties. It also uses to evaluate the flexibility index accurately while requiring short computation time (among the objectives of power system operation). Then, the results demonstrate its capabilities in enhancing various aspects of the network, including economic, environmental, operational, security, and flexibility indicators. The design helps derive flexibility conditions of almost 100%. It improves the operating cost, energy loss, voltage deviation, amount of pollution emission, and voltage security index by approximately 23%, 29%, 47%, 51%, and 13% compared with power flow studies.

Scheduling method for aggregated photovoltaic‐battery systems considering information notified by distribution system operator

AbstractIn recent years, the aggregation business has gained a lot of attention in Japan. Aggregators will make contracts with customers with photovoltaic (PV) power systems and battery energy storage systems (BESSs) including electric vehicles (EVs) to participate in electricity markets. Aggregators might have to pay electricity supply‐demand imbalance charges when generated and consumed energies contracted at the day‐ahead supply‐demand market are different from those at the current day operation due to distribution network constraints such as voltage and power flow limitations. Therefore, the information on network constraints is very important for aggregators to determine their day‐ahead schedules. In this paper, we evaluated the relationship between aggregator supply‐demand schedules and the distribution system operation. It was assumed that the reverse power flow limitations due to network constraints are notified to the aggregators by distribution system operator (DSO). Two cases of the aggregators’ schedules of PV systems and stationary BESSs were compared in the simulations. In addition, aggregator strategies for making adequate schedules were evaluated.

Privacy protected product differentiation through smart contracts based on bilateral negotiations in peer-to-peer transactive energy markets

Local peer-to-peer (P2P) energy markets have decentralized nature with bilateral negotiations and possibility of product differentiation to respect market participants’ preferences for attributes such as energy source. An extension is presented for a Transactive Energy based decentralized P2P market framework to support product differentiation along with a Hyperledger Fabric (HPLF) open-source blockchain network integration to enable secure P2P energy transactions while maintaining distribution line power flow limits. HPLF is chosen as its permissioned nature enables recording of higher number of transactions per second compared to famous blockchain ledgers such as Bitcoin. There is a need to protect the transaction details while recording on common blockchain ledger as the same buyer may be offering different energy rates to different sellers. Market clearing logic is encoded into smart contracts and a novel smart contract approach is proposed that serves dual purpose. First, it automatically verifies the negotiated energy prices and quantities among buyer–seller pair to validate transactions. Second, it records verified market transactions on common immutable blockchain using private data stores without exposing the transaction details while maintaining verifiability. Numerical co-simulation studies with standard HPLF integration show that participants can inherently align their bidding strategies to suit their product differentiation preferences such as higher prices for renewable energy producers (REPs). Detailed results describing the recording of energy transaction on HPLF ledger as transfer of virtual asset are included to demonstrate operation of proposed smart contract. Further, multi-step market clearing results show that the revenue for such REPs can be increased significantly.

Scheduling Method for Aggregated Photovoltaic-Battery Systems Considering Information Notified by Distribution System Operator

In recent years, the aggregation business has gained a lot of attention in Japan. Aggregators will make contracts with customers with photovoltaic (PV) power systems and battery energy storage systems (BESSs) including electric vehicles (EVs) to participate in electricity markets. Aggregators might have to pay electricity supply-demand imbalance charges when generated and consumed energies contracted at the day-ahead supply-demand market are different from those at the current day operation due to distribution network constraints such as voltage and power flow limitations. Therefore, the information on network constraints is very important for aggregators to determine their day-ahead schedules. In this paper, we evaluated the relationship between aggregator supply-demand schedules and the distribution system operation. It was assumed that the reverse power flow limitations due to network constraints are notified to the aggregators by distribution system operator (DSO). Two cases of the aggregators' schedules of PV systems and stationary BESSs were compared in the simulations. In addition, aggregator strategies for making adequate schedules were evaluated.

Robust Allocation of FACTS Devices in Coordinated Transmission and Generation Expansion Planning considering Renewable Resources and Demand Response Programs

Nowadays, the use of demand response programs (DRPs) in a variety of long-term and short-term planning problems has been explored. In this paper, a generation and transmission expansion planning (GTEP) model along with FACTS device allocation is presented. Furthermore, demand response programs are taken into account for more load flexibility. The proposed model is presented as a multi-objective minimizing problem considering emission, cost, and voltage security index. Furthermore, the conventional Pareto optimization is adopted using fuzzy weighted sum method (FWSM) to achieve a single-objective model. The final problem is constrained by equations of alternative current power flow, operation and voltage security limits, planning model of shunt FACTS devices, and operation of the DRP. Adaptive robust optimization (ARO) is used to reach suitable models for the active power of renewable resources and power consumption. As a main search algorithm, a hybrid combination of water cycle algorithm (WCA) and ant lion optimization (ALO) is proposed to find the optimum solution with a small standard deviation. The problem is tested on different standard IEEE systems, and the results validate the operation and network security improvement due to optimal location of FACTS devices. According to the results, the economic and environmental status of the network has also improved.

Bi-level Optimizations of Multi-Components Virtual Power Plant for Economic Dispatch and Renewable Utilization

The significant injections of renewable power have been leading to risks of safety and stability of power system by curtailing system inertia. Based on the requirement of ‘carbon neutralization’ policy, independent participants including energy storage (ES), load demand response (LDR) and virtual power plant (VPP) are introduced into system as innovative ancillary service methods. This paper proposes a bi-level optimization method to achieve the economic dispatch of multi-components virtual power plant consisting with gas generator, PV, wind turbine, energy storage and electricity vehicle in the day-ahead optimization, while achieving the maximum renewable power utilization in the real-time optimization by adjusting dispatch plan. This method considers constraints including solar/wind power forecasting, load level forecasting, ES charging/discharging constraints, and DC power flow limits. A case study based on practical project is present, showing that the bi-level optimization is capable of achieving diversified resources aggregation in extended region, as well as the economic optimization and green energy consumption target.

Multi-area economic emission dispatch for large-scale multi-fueled power plants contemplating inter-connected grid tie-lines power flow limitations

The purpose of this research is to investigate the multi-area economic emission dispatch problem (MEEDP) in the presence of renewable energy resources (RES) to improve the energy sustainability and climatic benefits. MEEDP is a multi-objective problem in smart grids, with the purpose of minimizing the operating costs and emissions of thermal units. RES have made a substantial contribution to greenhouse gases emission control and environmental sustainability. The integration of RES into conventional grids, which is becoming increasingly prevalent, spread the research scope of MEEDP and needs to be re-examined. This work considers two renewable sources (wind and solar) along with thermal plants subjected to significant number of previously uncombined system level limitations such as power capacity limit, prohibited zones, transmission network losses, dynamic ramp limits, tie-line limits and multiple fueling options. The operating cost is computed as summation of predictive and stochastic components. The predictive part is calculated by utilization of cumulative distribution function for each wind and solar system. A swarm intelligence-based crow search optimization algorithm (CSOA) is modeled to handle the complex constrained MEEDP with adjusted predictive part of RES. Six benchmark test systems with multi-dimensional constraints have been chosen to validate the adaptability and efficacy of the presented approach. Regardless of the complexity of the problem, the proposed approach provides the best feasible solution with a finer convergence rate. Finally, the simulation results depict that the integration of the corresponding system constraints gives legitimacy to the system and delivers reliable output.

Optimal linearized operation of electric railway system in the presence of flexible renewable sources and switchable capacitor banks considering supply and demand sides’ uncertainties

The electric train (ET) is a great energy consumer in an electrical network, which regenerates a high amount of energy into the grid at the braking moment. Therefore, in this paper, the optimal operation of the electric railway system is proposed considering renewable sources, hybrid storage systems (battery and ultracapacitor units), switchable capacitor banks, and regenerative braking energy of ET at the same time to attain a favorable economic, environmental and operational condition. Renewable sources reduce operating costs and environmental pollution. The storage system is utilized as a flexible source along with renewable generations. The ultracapacitor stores the ET braking energy, and the capacitor bank regulates the voltage. The scheme minimizes the expected network operating cost, while the AC optimal power flow and other technical limits are considered. This problem is nonlinear, and a linearized approximation formulation is implemented to achieve a unique optimal solution with the best and minimum computation time. The unscented transformation method is used to model uncertainties of load, ETs’ energy consumption, and renewable energies. Finally, by evaluating the numerical results, it is illustrated that the efficiency of the proposed method simultaneously improves the economic, environmental, and operational features of the network.

Integration of Solar Photovoltaic Distributed Generators in Distribution Networks Based on Site’s Condition

The significance of Distributed Generators (DGs) in the technical and economic operations of electric power distribution systems cannot be overemphasized in recent times. This is essential as a result of the incessant increase in electrical energy demand, which is becoming considerably difficult to meet with the conventional means of energy supply. Thus, DGs offer better alternatives for providing a quality supply of energy near the site of consumption. This type of energy supply is cleaner and cheaper most of the time due to the lessened transmission losses, which consequently reduced the cost of operation at the transmission and distribution levels of the power system. In this work, an approach for placement and sizing of solar PV DGs into radial distribution networks (RDN) based on the solar PV capacity factor of the site was analyzed using particle swarm optimization. The aim of this study is to analyze the effect of the approach on the real and reactive power losses within the network as well as the bus voltage profile. Constraints on credible system operation parameters, which includes bus voltage limits, power balance, and power flow limits, are considered in the formulation of the optimization problem. In order to verify the viability of the deployed approach, steady-state performance analyses were executed on IEEE 33-bus RDN; and the results obtained were compared with the results from other approaches reported in the literature.

Equilibrium Optimizer-Based Approach of PV Generation Planning in a Distribution System for Maximizing Hosting Capacity

This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system’s PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).

Reduction of operational restrictions of the transmission system in electrical systems through facts implementation

Operational constraints cause an increase in complexity, operating cost, and environmental and socioeconomic impact on electrical systems; operational constraints can be due to power flow, voltage, or frequency deviations. These restrictions force the grid operator to perform unplanned actions. This article identifies the various Alternating Current Flexible Transmission Systems (FACTS) devices with their characteristics, attributes, and implementation methods to lessen operating constraints on isolated and relatively small transmission systems with frequent power flow limit violations and voltage deviations. The case study is carried out in the National Interconnected Electric System (SENI) of the Dominican Republic, where the Fast Stability Voltage Index is used as an indicator to see the impact of the implementation of FACT to mitigate the operational restrictions of the electric system. Simulations are performed in the PowerFactory DIgSILENT software to obtain the results when modeling the interconnected FACTS devices on the transmission lines. The use of FACTS reduces the loadability of transmission lines, which is an essential factor in the emergence of power flow control and voltage constraints in isolated power systems.

Network constrained optimal performance of DER and CHP based micro-grids within an integrated active-reactive and heat powers scheduling

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

Improved black widow-bear smell search algorithm (IBWBSA) for optimal planning and operation of distributed generators in distribution system

Purpose This paper aims to propose, the multi-objective method for optimal planning and operation of distributed generators (DGs) on distribution system (DS) using hybrid technique is proposed. Design/methodology/approach The proposed hybrid technique denotes hybrid wrapper of black widow optimization algorithm (BWOA) and bear smell search algorithm (BSSA). BWOA accelerates the convergence speed with combination of the search strategy of BSSA; hence, it is named as improved black widow-bear smell search algorithm (IBWBSA) technique. Findings The multiple-objective operation denotes reducing generation cost, power loss, voltage deviation with optimally planning and operating the DS. For setting up the DG units on DS, IBWBSA technique is equipped to simultaneously reconfigure and find the optimal areas. Originality/value In this planning model, the constraints are power balance, obvious power flow limit, bus voltage, distribution substation’s capacity and cost. Then, proposed multiple-objective hybrid method to plan electrical distribution scheme is executed in the MATLAB/Simulink work site.

Investigation of Impact of Contingencies on Power Plant and Transmission Lines

Contingency analysis are widely applied to predict the effect of outages in power systems, like tripping of equipment in power plants and transmission lines. Using off line analysis to predict the effect of individual contingency is a tedious task on power system containing large number of components. Practically, only selected contingencies will lead to severe conditions in power system, like violation of voltage and active power flow limits. Simultaneously, the value of active power flow before and after severe transmission and power plant contingencies was analysed using Genetic Eigenvalue Analysis Technique. This was achieved by simulating the Simulink of Nigerian 330KV 48 bus power system using m-file programme in MATLAB environment.The result of the simulation for the power flow solution for transmission line outage contingencies shows that the voltage trajectory at bus 11 stood at 0.3934 p.u, at bus 15 is0.4986 p.u, and bus of 23 is 0.4647 p.u while for the contingencies on power plant, there voltage trajectories stood at 0.2342 p.u for bus 11, 0.3987 p.u for bus 23. The result shows that the impact on power plant is higher than that of transmission line by 40%, 20%, 14%for sampled buses 11, 15, and 23 respectively. Keywords: Power Stability- Genetic Eigenvalue, Load flow DOI: 10.7176/JETP/11-1-04 Publication date: January 31 st 2021

Hybrid Optimization Toward Proactive Resilient Microgrid Scheduling

Microgrids are one important lever to increase power system resilience and to tightly integrate renewable energies at the same time. Commonly, an optimization-based proactive scheduling controls assets in advance in a cost-effective way and ensures that contingencies may be successfully mitigated. However, often strong simplifications are introduced to manage the high computational complexity of scheduling, which can adversely impact fault mitigation. To consider essential phenomena such as power flow limitations and low-level control capabilities in detail, a novel hybrid scheduling approach is presented that integrates mathematical programming and arbitrary nonlinear constraint models via decision trees. A detailed case study compares the new method to an extended hybrid scheduling approach from literature. It is demonstrated that hybrid optimization can efficiently solve proactive resilient scheduling problems and that the tree-based algorithm provides a feasible solution, even in case the reference algorithm fails. Details on the convergence of both algorithms give further insights into the working principles and show that the novel method quickly finds a feasible solution that is successively improved afterwards. By the novel combination of highly-developed solvers for both mathematical programming and detailed asset models it is expected that this study further supports the operation of power systems and reduces costly reserve requirements.