Priority List Method Research Articles

Optimisation of Economic and Environmental Dispatch of Power System with and without Renewable Energy Sources

Combined Economic and Environmental load dispatch is critical to the functioning of the power grid, and many models have been developed to address these issues using various techniques. Specially, soft computing methods have recently risen in popularity and have been used in a variety of popular and practical applications. The aim of this paper is to determine the benefits of applying Particle Swarm Optimization (PSO) to the Combined Economic and Environmental Dispatch (CEED) problem in particular. Here, an attempt has been made to find the minimum cost of generation of a system of thermal and solar power plants. The problem is multi-objective and is converted into a single objective function using weighted sum method. Analysis is also done with and without unit commitment using Priority List method and the results are compared. The analyses has been done in MATLAB tool using six thermal power generators and thirteen solar power plants.

Profit evaluation inclusive of reserve pricing for renewable-integrated GENCOs

Abstract Under the deregulated market scenario, the electric power industry is rapidly getting transformed due to integration of increasing renewable generation. Profit maximization has always been of highest priority for all the stakeholders. The generation planning problem is an important problem in the operation and planning of power systems which is required to be solved to accomplish the maximum profit. The individual GENeration COmpanies (GENCOs) are envisioning their most profitable Unit Commitment (UC) in the light of the available forecasts of availability of solar and wind power, energy prices and reserve prices during a given scheduling period. This paper presents a UC strategy for GENCOs’ profit with a generation portfolio comprising thermal generation, wind generation, and solar PV generation in different combinations. Priority List Method (PLM) and Modified Particle Swarm Optimization (PSO) with Time-Varying Acceleration Coefficients (PSO-TVAC) solution technique is deployed on the MATLAB platform. The aim is to maximize the profit while satisfying the load demand together with maintaining sufficient reserve at each hour. Sensitivity analysis of profit has also been carried out to showcase the impact of different pricing techniques, different parameters chosen and the different bidding prices for the power dispatched as well as for the reserve power maintained. The observations that emerged through the presented solution model may help develop an insight to the GENCOs to gain maximum profits.

Binary Particles Swarm Optimization for Power Plant Schedule by Considering “Take or Pay” Contract

This study discuss 24-hours load scheduling to get the minimum generation cost. Fuel cost in load scheduling need to be considered, because fuel and power suppliers are bound by take or pay schedule. In addition load scheduling considering heat rate, start-up cost, minimum up time and minimum down time. Load scheduling calculate by priority list method that is completed using artificial intelligence, Binary Particle Swarm Optimization (BPSO). The data used in this study is IEEE 30 bus and Java-Bali 500 kV.

An Efficient Approach to Schedule Generating Units of Combined Heat and Power (CHP) Generating System

The unit commitment (UC) of thermal, heat and combined heat and power (CHP) is one of the critical issues of power systems which have an essential role in the economic performance of power systems. The mutual dependency of heat and power in the CHP unit has increased the complexity of the UC problem. In this research work, two modes of CHP units, extraction and back-pressure, have been undertaken, which offers flexibility as compared to each other. The added value of operating modes of CHP units in the UC problem is in the infancy stage. Afterwards, the optimization technique has been applied to solve the CHP UC problem. The binary particle swarm optimization is used to to update unit status, and to search optimal generation schedule priority list method has been employed. This method has been applied to solve economic and profit models of CHP-UC problem. One test system that has been undertaken consists of ten conventional thermal units, one CHP, and one heat only unit. The obtained results of economic and profit models reveal the importance of the CHP unit under different modes.

Unit commitment based on modified firefly algorithm

Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on the problem to be solved. Firefly algorithm is a suitable method for power system operation scheduling. This paper presents a modified firefly algorithm to address unit commitment issues. Generally, two steps are involved in solving unit commitment problems. The first step determines the generating units to be operated, and the second step calculates the amount of demand-sharing among the units (obtained from the first step) to minimize the cost that corresponds to the load demand and constraints. In this work, the priority list method was used in the first step and the second step adopted the modified firefly algorithm. Ten generators were selected to test the proposed method, while the values of the cost function were regarded as criteria to gauge and compare the modified firefly algorithm with the classical firefly algorithm and particle swarm optimization algorithms. Results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation.

PSO Based Dispatch and Control of Hybrid Power System with PV-Diesel Generator- A Prototype

A micro-grid is a small-scale power grid that can operate independently or in conjunction with the area’s main electrical grid. Micro-grid paves way to power remote locations and effectively integrate various sources of distributed generation, including renewable. In this paper, an economical way of dispatching PV power and power generated by generators in islanded micro-grid is proposed. Here, part of the micro-grid demand is supplied by PV power station. The demand which cannot be supplied by the PV panel, are dispatched between DGs by solving unit commitment and economic dispatch problems. Unit commitment is carried out using priority list method and economic dispatch particle Swarm optimization. A hardware prototype for the proposed micro-grid dispatch methodology has been developed. Here, commitment and de­commitment of the sources and demands is carried out by generating control signals denoting the ON/OFF status to the sources for different intervals.

Optimal Unit Commitment by Considering High Penetration of Renewable Energy and Ramp Rate of Thermal Units-A case study in Taiwan

When large amounts of wind power and solar photovoltaic (PV) power are integrated into an independent power grid, the intermittent renewable energy destabilizes power output. Therefore, this study explored the unit commitment (UC) optimization problem; the ramp rate was applied to solve problems with 30 and 10 min of power shortage. The data of actual unit parameters were provided by the Taiwan Power Company. The advanced priority list method was used together with a combination of a generalized Lagrangian relaxation algorithm and a random feasible directions algorithm to solve a large-scale nonlinear mixed-integer programming UC problem to avoid local and infeasible solutions. The results showed that the proposed algorithm was superior to improved particle swarm optimization (IPSO) and simulated annealing (SA) in terms of the minimization of computation time and power generation cost. The proposed method and UC results can be effective information for unit dispatch by power companies to reduce the investment costs of power grids and the possibility of renewable energy being disconnected from the power system. Thus, the proposed method can increase the flexibility of unit dispatch and the proportion of renewable energy in power generation.

A priority list-based fuzzy logic controller for short-term unit commitment problem

This paper presents a fuzzy logic-based priority list method to solve the unit commitment problem. The priority list order is selected according to the maximum output power rating of the generating units while satisfying all the constraints over a period of time. The turn on (commitment) and turn off (de-commitment) decision of thermal generating units is handled by priority list order with fuzzy logic algorithm. A fuzzy optimisation-based approach is developed to find the optimal solution by using fuzzy operations and if-then rules. The objective of the optimisation is to achieve minimum operating cost by scheduling of generator units. In solving the unit commitment problem, reliability and feasibility constraints are checked for the planning interval. This method is implemented on standard IEEE 69-bus, 11-thermal generator system. Use of the fuzzy logic controller with priority list method enables the handling of uncertainties in load forecast in an efficient manner.

A priority list-based fuzzy logic controller for short-term unit commitment problem

This paper presents a fuzzy logic-based priority list method to solve the unit commitment problem. The priority list order is selected according to the maximum output power rating of the generating units while satisfying all the constraints over a period of time. The turn on (commitment) and turn off (de-commitment) decision of thermal generating units is handled by priority list order with fuzzy logic algorithm. A fuzzy optimisation-based approach is developed to find the optimal solution by using fuzzy operations and if-then rules. The objective of the optimisation is to achieve minimum operating cost by scheduling of generator units. In solving the unit commitment problem, reliability and feasibility constraints are checked for the planning interval. This method is implemented on standard IEEE 69-bus, 11-thermal generator system. Use of the fuzzy logic controller with priority list method enables the handling of uncertainties in load forecast in an efficient manner.

A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation

Environmental concerns have caused application of renewable energy sources as a clean appropriated alternative for conventional thermal generators in power system planning. These uncertain and variable sources have challenged the power system scheduling to ensure that it still remains reliable and economical. Also, plug-in electric vehicles may potentially alter load demand in uncontrolled charging mode and impose new challenges to the unit commitment problem. This paper demonstrates the fast heuristic method based on priority list to solve the stochastic unit commitment problem and applied it to a basic 10-unit case study system accompaniment with an electric vehicle parking lot, a wind farm and a solar farm over a 24-h time horizon. As it is reported, scenario generation with Monte Carlo simulation can relatively compensate for the intermittent behavior of renewable energy sources and result in more economical and robust planning. Moreover, the integration of plug-in electric vehicles and their controlled charging/discharging is considered. Thermal units are scheduled by priority list method with their relevant constraints such as minimum up/down time, spinning reserve, load demand and capacity limitation. The simulations results verify that the penetration of the mentioned renewable energy sources can improve the operational cost and computation time, effectively.

Optimal Scheduling Strategy for Domestic Electric Water Heaters Based on the Temperature State Priority List

With the rapid growth of thermostatically controlled loads, active power fluctuation and peak demand growth within an autonomous micro-grid become serious problems. This paper tries to suppress power fluctuation and shave peak demand for a micro-grid through optimizing domestic electric water heaters (controllable load). In this paper, domestic electric water heater models are first built to optimize power flow within a single water heater. Subsequently, the Monte Carlo method is proposed to simulate power consumption of a cluster of domestic electric water heaters. After that, the temperature state priority list method is presented to suppress power flow and shave peak demand for a given micro-grid. Optimization results show that the proposed temperature state priority list method can reduce peak demand by 12.5%. However, it has a wider active power fluctuation range and needs a longer reaction time compared with the simplified temperature state priority list method. In addition, the optimization results show that by increasing the number of controllable loads participating in load scheduling, active power fluctuation can be reduced and the maximum active power of the given micro-grid can be cut. However, to achieve this, about 1.2% of extra electrical energy needs to be generated by the external grid.

Unit Commitment by an Improved Priority List Method

Unit Commitment by an Improved Priority List Method

New probabilistic method for solving economic dispatch and unit commitment problems incorporating uncertainty due to renewable energy integration

Abstract In this paper, a methodology to solve Unit Commitment (UC) problem from a probabilistic perspective is developed and illustrated. The method presented is based on solving the Economic Dispatch (ED) problem describing the Probability Distribution Function (PDF) of the output power of thermal generators, energy not supplied, excess of electricity, Generation Cost (GC), and Spinning Reserve (SR). The obtained ED solution is combined with Priority List (PL) method in order to solve UC problem probabilistically, giving especial attention to the probability of providing a determined amount of SR at each time step. Three case studies are analysed; the first case study explains how PDF of SR can be used as a metric to decide the amount of power that should be committed; while in the second and third case studies, two systems of 10-units and 110-units are analysed in order to evaluate the quality of the obtained solution from the proposed approach. Results are thoroughly compared to those offered by a stochastic programming approach based on mixed-integer linear programming formulation, observing a difference on GCs between 1.41% and 1.43% for the 10-units system, and between 3.75% and 4.5% for the 110-units system, depending on the chosen significance level of the probabilistic analysis.

Hierarchical management for integrated community energy systems

Due to the presence of combined heat and power plants (CHP) and thermostatically control loads, heat, natural gas, and electric power systems are tightly coupled in community areas. However, the coordination among these systems has not yet been fully researched, especially with the integration of renewable energy. This paper aims to develop a hierarchical approach for an integrated community energy system (ICES). The proposed hierarchical framework is presented as day-ahead scheduling and two-layer intra-hour adjustment systems. Two objectives, namely the operating cost minimization and tie-line power smoothing, are integrated into the framework. In the intra-hour adjustment, a master–client structure is designed. The CHP and thermostatically controlled loads are coordinated by a method with two different time scales in order to execute the schedule and handle uncertainties from the load demand and the renewable generation. To obtain the optimal set-points for the CHP, an integrated optimal power flow method is developed, which also incorporates three-phase electric power flow and natural gas flow constraints. Furthermore, based on a time priority list method, a three-phase demand response approach is proposed to dispatch thermostatically controlled loads at different phases and locations. Numerical studies confirm that the ICES can be economically operated, and the tie-line power between the ICES and external energy network can be effectively smoothed.

Random feasible directions algorithm with a generalized Lagrangian relaxation algorithm for solving unit commitment problem

This study has developed a hybrid algorithm by combining the advantages of both the generalized Lagrangian multiplier method and the random feasible directions algorithm, to solve the unit commitment problem that exists in the large-scale nonlinear mixed integer programming of a power system. Furthermore, a filtering constraint implicit enumeration algorithm (FCIEA) incorporated with a rule-based selection strategy was used to avoid a local optimal or infeasible solution and to improve the accuracy and solution speed. The rule-based selection strategy was established on both the FCIEA and the advanced priority list method. To thoroughly demonstrate the proposed algorithm, three case studies were simulated: IEEE-10 units, a short-term dispatch of an autonomous renewable energy generation system, and a unit commitment of the Taiwan power system. Once the Lungmen nuclear power plant is commercially operated, critical concerns, such as the change of operating reserve demand, the safety, reliability, and economy of system operations, and the policy of dispatching ample operating reserves to different units, will force a redispatch of the current units to meet new economic costs. All of the results obtained in this study were tested under a practical power system. The result can provide Taipower Company a guideline for dispatch and scheduling. In addition to comparing the proposed algorithm with those in previous studies, three self-compiled programs were adopted, namely, simulating annealing and particle swarm optimization, to verify the effectiveness of the proposed algorithm and provide evidence for comparison.

Voltage stability enhancement using thermostatically controlled appliances as a comfort-constrained virtual generator

Conventional direct load‐shedding for achieving static voltage stability lacks considerations on both customer comfort and energy efficiency, resulting in higher cost and emission. A novel security‐based, optimal load‐shedding strategy considering customer comfort settings is presented in this paper. A temperature priority list method is used to model the virtual generator (VG) consisting of thermostatically controlled appliances (TCAs). To illustrate the control process and performance evaluation of the proposed load‐shedding scheme, a modified IEEE 6‐bus test system is used. Three heat, ventilation, and air‐conditioning (HVAC) groups are numerically simulated to respond to optimal load shedding signals. Reduced responsive load population, variations of temperature dead‐bands, and different outdoor temperature profiles are modeled to evaluate the capacity variations of the VG and its control performance. The results demonstrate that TCAs can provide satisfactory voltage stability.

Reinforcement Learning in Adaptive Control of Power System Generation

Considering our depleting resources, efficient energy production and transmission is the need of the hour. This paper focuses on the concept of using Reinforcement Learning (RL) to control the power systems unit commitment and economic dispatch problem. The idea of reinforcement learning strives to present an ever optimal system even when there are load fluctuations. This is done by training the agent (system), thereby enriching its knowledge base which ensures that even without manual intervention all the available resources are used judiciously. Also the agent learns to reach long term objective of minimizing cost by autonomous optimization. A model free reinforcement learning method called, Q learning is used to find the cost at various loadings and is compared with the conventional priority list method and the performance improvement due to Q learning is proved.

An improved priority list and neighborhood search method for unit commitment

This paper presents an improved priority list and neighborhood search (IPL-NS) method for ramp rate constrained unit commitment (RUC). The IPL-NS consists of two steps. At the first step, an IPL based on a mixed integer linear programming (MILP) formulation for UC is proposed to get unit status for UC without ramp rate constraints. At the second step, a neighborhood search (NS) algorithm based on three neighborhoods is produced to get unit status for RUC. The IPL-NS approach is applied to solve several popular test systems of up to 1000 units and the simulation results confirm the superiority of the proposed method. At the same time, the numerical results show that the proposed IPL can commit the most economic unit first much better than other existing PL methods. The results also indicate that the presented NS can handle the ramp rate constraints efficiently.

A new scenario generation-based method to solve the unit commitment problem with high penetration of renewable energies

Abstract Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays this problem is solved using the Monte Carlo Simulation (MCS) approach, which allows the consideration of important statistical characteristics of wind and solar power production, such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. In this paper, a new model of the unit scheduling of power systems with significant renewable power generation based on the scenario generation/reduction method combined with the priority list (PL) method is proposed that finds the probability distribution function (PDF) of a determined generator be committed or not. This approach allows the recognition of the role of each generation unit on the day-ahead unit commitment (UC) problem with a probabilistic point of view, which is important for acquiring a cost-effective and reliable solution. The capabilities and performance of the proposed approach are illustrated through the analysis of a study case, where the spinning reserve requirements are probabilistically verified with success.

Research on Scheduling Optimization of Wind Power Integrated System

This paper studied the scheduling optimization problem of the wind power integrated system. A power system scheduling model was established aiming at minimizing the generation cost and reducing pollution. A method combined priority-list method (PL) with multi-objective particle swarm optimization (MOPSO) was proposed to overcome the blindness of particle swarm optimization (PSO) and improve the calculation speed. The priority-list method was used to sort the units. The simple economic dispatch result was taken as the initial location of particles. Adaptive mutation was introduced to guarantee the diversity of particles. The simulation result indicates that the method proposed in this paper is feasible and reasonable.