Scheduling Of Generating Units Research Articles

Multi-level optimal energy management strategy for a grid tied microgrid considering uncertainty in weather conditions and load

Microgrids require efficient energy management systems to optimize the operation of microgrid sources and achieve economic efficiency. Bi-level energy management model is proposed in this paper to minimize the operational cost of a grid-tied microgrid under load variations and uncertainties in renewable sources while satisfying the various technical constraints. The first level is day ahead scheduling of generation units based on day ahead forecasting of renewable energy sources and load demand. In this paper, a recent meta-heuristic algorithm called Coronavirus Herd Immunity Optimizer (CHIO) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem, while the Lagrange multiplier method is used to determine the set-point of the Diesel Generator (DG). The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. The obtained results show that the CHIO-based bi-level EMS demonstrates an optimal economic operation for a grid-connected microgrid in real-time when there are uncertainties in weather, utility tariffs, and load forecasts.

MPC-based energy management strategy for a substation PEDF System

According to the optimal scheduling and balanced utilization of power generation units, energy storage, and flexible loads in the PEDF(photovoltaic, energy storage, direct current and flexibility) system of a substation, and realizing stable operation with different time scales and operation modes, this paper proposes a hierarchical control framework, which includes energy management layer, power conversion layer, stability control layer, and an MPC-based energy management strategy based control. Among them, the energy management system uses the prediction of the absorbed power and current of the distributed generation units and loads, and based on the technology of model predictive control to generate the optimal reference power and decision variables for the system to decide the on/off of the system’s dispatchable generation unit and the operation mode of photovoltaic and battery. Finally, the proposed control architecture and strategy are validated by a substation PEDF system.

Multi-objective Optimal Scheduling Analysis of Power System Based on Improved Particle Swarm Algorithm

Economic Environmental Dispatching (EED) in power systems is a multi-variable, strongly constrained, non-convex, multi-objective optimization problem that is difficult to properly handle using traditional methods. However, the application of particle swarm optimization algorithms may result in insufficient population diversity and easy to fall into local optimization problems. Therefore, this paper proposes an adaptive backbone multi-objective particle swarm optimization (ABBMOPSO) method to solve the economic and environmental scheduling problems of power systems. This paper first analyzes the topology and computational flow of particle swarm optimization algorithms, and then constructs a multi-objective optimization research framework that integrates Pareto optimization principles for the scheduling of power generation units. The execution algorithm is the improved multi-objective particle swarm optimization algorithm (MOPSO). This paper establishes a mathematical model for the economic and environmental scheduling of power systems, which optimizes conflicting fuel cost functions and pollutant emission functions simultaneously, taking into account nonlinear constraints such as load balance constraints and unit operation constraints. The improved ABBMOPSO algorithm is used to optimize the solution to improve the global search ability of the EED model. The simulation data of seven units show that the ABBMOPSO algorithm has a minimum power generation cost of 588.1 $/h and a minimum pollutant emission of 0.192 t/h, which is significantly superior to other algorithms and reduces the number of iterations, with good feasibility.

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems

The optimal operation of modern power systems aims at achieving the increased power demand requirements regarding economic and technical aspects. Another concern is preserving the emissions within the environmental limitations. In this regard, this paper aims at finding the optimal scheduling of power generation units that are able to meet the load requirements based on a multi-objective optimal power flow framework. In the proposed multi-objective framework, objective functions, technical economical, and emissions are considered. The solution methodology is performed based on a developed turbulent flow of a water-based optimizer (TFWO). Single and multi-objective functions are employed to minimize the cost of fuel, emission level, power losses, enhance voltage deviation, and voltage stability index. The proposed algorithm is tested and investigated on the IEEE 30-bus and 57-bus systems, and 17 cases are studied. Four additional cases studied are applied on four large scale test systems to prove the high scalability of the proposed solution methodology. Evaluation of the effectiveness and robustness of the proposed TFWO is proven through a comparison of the simulation results, convergence rate, and statistical indices to other well-known recent algorithms in the literature. We concluded from the current study that TFWO is efficient, effective, robust, and superior in solving OPF optimization problems. It has better convergence rates compared with other well-known algorithms with significant technical and economical improvements. A reduction in the range of 4.6–33.12% is achieved by the proposed TFWO for the large scale tested system. For the tested system, the proposed solution methodology leads to a more competitive solution with significant improvement in the techno-economic aspects.

Bootstrap aggregating approach to short-term load forecasting using meteorological parameters for demand side management in the North-Eastern Region of India

Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland’s load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that the Bagging methods outperform other models with respect to mean squared error and mean absolute percentage error.

Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response

The periodic outage planning of generation units for maintenance services is critical for their economic operation. Ignoring it can lead to premature degradation, high repair costs, and system reliability deterioration due to unexpected outages. Accordingly, generation maintenance scheduling (GMS) can optimally handle this challenge. In a power system with combined heat and power (CHP), although CHP units can efficiently supply power and heat demand, continuous operation of such units can cause an increase in unexpected failure rate and repair costs. Accordingly, this research accomplishes GMS coordination in CHP-integrated energy systems to optimally determine the maintenance plan of units. Firstly, the yearly GMS is planned and then the short-term generation scheduling is optimized. The proposed GMS problem is modeled as a mixed-integer nonlinear programming problem while considering the technical constraints, CHP feasible operation region, and power and heat balance. Moreover, the uncertainty in the market price is considered and the CVaR measure is employed to deal with the risks in the GMS. Furthermore, demand response is also assumed in the short-term generation scheduling while evaluating the impact of the yearly GMS. Numerical simulations with scenario-based optimization are used to evaluate the proposed CHP GMS problem with uncertainty in market price.

An integrated framework for dynamic capacity withholding assessment considering commitment strategies of generation companies

This paper addresses an integrated framework for the dynamic capacity withholding assessment of an independent system operator that determines the mid-term maintenance scheduling of generation companies and day-ahead scheduling of wholesale market participants. The main contribution of this research is that two dynamic capacity-withholding indices are proposed for mid-term and day-ahead scheduling of generation companies that estimate the dynamic capacity withholding opportunities of generation units in an ex-ante manner. The proposed framework is another contribution of this research that uses a four-stage optimization process that the system operator can detect and prevent the formation of withholding groups. The optimal maintenance scheduling from the generation companies viewpoint is assessed in the first-stage problem that considers different mid-term withholding opportunities. The optimal mid-term maintenance scheduling is carried out in the second-stage problem that recognizes and rejects the dynamic capacity withholding of generation companies. The optimal scheduling of day-ahead generation companies considering their dynamic capacity withholding is the third contribution of this paper that optimizes the scheduling of generation units for day-ahead horizon considering responsive loads. The proposed method is applied to 30-bus, 57-bus and 118-bus IEEE test systems. A full competition algorithm is also carried out to evaluate the competition states of generation companies. The proposed algorithm detected that the dynamic capacity withholding might lead to increase of nodal price by about 279.22%, 764.43%, and 851.2% for 30-bus, 57-bus, and 118-bus IEEE test systems with respect to the non-capacity withholding conditions, respectively.

On the temporal granularity of joint energy-reserve markets in a high-RES system

The cost-efficient integration of variable renewable power (vRES) requires adequate operating reserve and energy markets. This paper focusses on the benefits of increasing the temporal granularity of joint energy-operating reserve markets in a case study on two high-vRES systems. Specifically, the impact of the frequency (i.e., how often reserves are sized and procured) and the resolution or contract duration (i.e., the length of the sizing and procurement blocks) is isolated by comparing year-long unit commitment and economic dispatch simulation results. We optimize the power system’s operation in consecutive stages: scheduling of generation units with procurement of reserves, intra-day adjustments of generation schedules and real-time activation of flexibility to compensate for imbalances. The results suggest that dynamic and more frequent sizing, limiting the reserve procurement contract duration and a higher procurement frequency allow reducing the total operating cost by 1.5–1.8%, while eliminating scarcity on reserve markets. These design changes facilitate the integration of intermittent renewables in reserve markets. Finally, some challenges following the proposed market design changes are highlighted.

Short-term electricity grid maximum demand forecasting with the ARIMAX-SVR Machine Learning Hybrid Model

This paper proposes and discusses the viability of a short-term grid maximum demand forecasting model combining autoregressive integrated moving average with regressors (ARIMAX) and support vector regression (SVR). Grid demand forecasting is essential to generation unit scheduling, maintenance planning and system security. Traditionally, grid demand is forecasted using multivariate linear regression models with parameters adjusted to past data. A disadvantage of the linear regression model is that the parameters require regular adjustment, otherwise the prediction accuracy will deteriorate over time. With recent advances in the field of machine learning and lower computational costs, the usage of machine learning in the power industry becomes increasingly practicable. The proposed model is a machine learning model that combines ARIMAX and SVR to exploit their respective effectiveness in predicting linear and non-linear data. In contrast to linear regression models, the machine learning model automatically updates itself when new data is included. The hybrid model is benchmarked against other forecasting models and demonstrated a marked improvement in accuracy, achieving RMSE of 67.7MW and MAPE of 1.32% in a seven-day forecast.

Short-term operation of microgrids with thermal and electrical loads under different uncertainties using information gap decision theory

The utilization of an Energy Management System (EMS) for the optimum scheduling of generation units, as well as demand side resources is essential due to the high penetration of Distributed Energy Resources (DERs) in microgrids (MGs), to achieve the desired objectives. As a result of the restructuring of the power systems and increasing the electricity prices during some periods in a day, demand side programs have been highly valuable by electricity customers. In this paper, a Demand Response (DR) model has been proposed to present the behavior of responsive controllable loads in response to the DR calls. Moreover, optimal scheduling of energy resources is developed for a typical MG by considering the presence of both electrical and thermal demands. Combined Heat and Power (CHP) units, boilers, wind turbines, storage devices, demand response resources (DRRs), as well as the power exchange possibility with the upstream wholesale market are the energy resources that have been considered as the portfolio of the decision maker. Furthermore, the uncertainty resources of the wind speeds and electrical load are handled by the Information Gap Decision Theory (IGDT) method. The performance of the proposed framework is comprehensively analyzed on the IEEE 33-bus test system. The advantage of the proposed methodology under the uncertainty conditions is analyzed by the Monte-Carlo simulation method when the different realization of the wind power and electrical load are considered.

Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach

Yearly generation maintenance scheduling (GMS) of generation units is important in each system such as combined heat and power (CHP)-based systems to decrease sudden failures and premature degradation of units. Imposing repair costs and reliability deterioration of system are the consequences of ignoring the GMS program. In this regard, this research accomplishes GMS inside CHP-based systems in order to determine the optimal intervals for predetermined maintenance required duration of CHPs and other units. In this paper, cost minimization is targeted, and violation of units’ technical constraints like feasible operation region of CHPs and power/heat demand balances are avoided by considering related constraints. Demand-response-based short-term generation scheduling is accomplished in this paper considering the maintenance intervals obtained in the long-term plan. Numerical simulation is performed and discussed in detail to evaluate the application of the suggested mixed-integer quadratic programming model that implemented in the General Algebraic Modeling System software package for optimization. Numerical simulation is performed to justify the model effectiveness. The results reveal that long-term maintenance scheduling considerably impacts short-term generation scheduling and total operation cost. Additionally, it is found that the demand response is effective from the cost perspective and changes the generation schedule.

Preventive maintenance mid-term scheduling of resources in multi-carrier energy systems

Multi carrier energy systems (MCES) have an assortment of energy carriers and production resources. These diverse energy carriers are relatively correlated, complicating the preventive maintenance scheduling of MCES. The complexity is more prominent in mid-term scheduling. The main challenge arises when there are shared units in all energy systems such as combined cooling, heating and power (CCHP) units. To achieve a reliable and optimized maintenance, the correlation between energy carriers should be taken into account. In this paper, a novel model is proposed for optimized maintenance scheduling of generation units in monopoly MCES. Using the proposed model, suitable universal outage time windows are obtained, whereby the total operation and maintenance costs are optimized. As a case study, the proposed model is examined on a test case comprising electrical, thermal, and cooling systems. Simulation results verified the meticulousness of the proposed model in mid-term maintenance scheduling while respecting reliability constraints.

Optimal scheduling of electric vehicles and photovoltaic systems in residential complexes under real-time pricing mechanism

Abstract Residential complexes (RCs) consisting of photovoltaic (PV) systems, wind turbines and electric vehicles (EVs), have been rapidly extended in energy systems in recent years. Optimal scheduling of local generation units can lead to economic improvement in RCs. In this regard, optimization of the performance of RCs appears to be necessary. Operators of RC energy systems equipped with local generation units can benefit from demand response programs (DRPs) to reduce their operating costs while satisfying energy demands. Within these programs, energy consumers are motivated to change their consumption in a way that economic targets are satisfied. In this paper, optimal operation of a grid-connected EV/PV RC energy system is studied under real-time pricing of a DRP. The studied RC energy system is equipped with PV units and EVs that can support RCs in supplying energy demands and providing economic benefits. The incorporated PV unit is modeled considering solar irradiance parameters and ambient temperature, which can lead to accurate simulation results. Moreover, the proposed model for EVs can enhance the efficient operation of RCs through optimal charge and discharge processes. Further, the optimal operation of energy systems integrated into RCs is modeled as mixed-integer linear programming (MILP). Additionally, a general algebraic modeling system (GAMS) is used to carry out simulations in different scenarios and the results are presented for comparison. For the studied test case, the total expected cost of RCs is reduced by 37.31%, which represents the influential impact of demand response on the optimal scheduling of DG units.

Stochastic Network-Constrained Unit Commitment to Determine Flexible Ramp Reserve for Handling Wind Power and Demand Uncertainties

The flexible ramp reserve of dispatchable generators is a key effective means to compensate the fast fluctuation of wind power generators and the forecast error of demand. This article proposes a stochastic network-constrained unit commitment (NCUC) model to determine, on daily horizon, the optimal schedule of generation units along with the required flexible ramp and spinning reserves under uncertainties of wind power and demand. Point estimate method (PEM) is utilized to reduce the number of scenarios that model wind power and demand uncertainties. In addition to PEM scenarios, some worst-case scenarios based on regional wind and demand variations are defined and incorporated in the proposed model. The NCUC model includes for these scenarios various technical constraints of generation units and operation constraints of the network. To ensure computational tractability of NCUC model, the ac network operation constraints are linearized. The proposed mixed integer linear programming NCUC model is tested using the IEEE 118-bus system. The obtained results show better units scheduling than the conventional (deterministic) NCUC model.

MILP-Based Short-Term Thermal Unit Commitment and Hydrothermal Scheduling Including Cascaded Reservoirs and Fuel Constraints

<span>Reservoirs are often built in cascade on the same river system, introducing inexorable constraints. It is therefore strategically important to scheme out an efficient commitment of thermal generation units along with the scheduling of hydro generation units for better operational efficiency, considering practical system conditions. This paper develops a comprehensive, unit-wise hydraulic model with reservoir and river system constraints, as well as gas constraints, with head effects, to commit thermal generation units and schedule hydro ones in the short-term. A mixed integer linear programming (MILP) methodology, using the branch and bound & cut (BB&C) algorithm, is employed to solve the resultant problem. Due to the detailed modelling of individual hydro units and cascaded dependent reservoirs, the problem size is substantially swollen. Multithread computing is invoked to accelerate the solution process. Simulation results, conducted on various test systems, reiterate that the developed MILP-based hydrothermal scheduling approach outperforms other techniques in terms of cost efficiency.</span>

Multi‐objective optimisation of generation maintenance scheduling in restructured power systems based on global criterion method

Generation maintenance scheduling (GMS) is one of the most important scheduling problems in the restructured power systems. The maintenance time interval of generation units is the crucial factor of GMS for an operation lifespan of generation units, particularly within the smart grid which needs high reliability. Accordingly, this study proposes a multi-objective-GMS (MO-GMS) optimisation model for maintenance scheduling of generation units based on the global criterion approach, adopting a suitable compromise function. The proposed MO-GMS model determines the maintenance intervals, aims to maximise both the generation company's (GenCo's) financial returns from selling electricity and the system reserve at every time interval from the independent system operator (ISO) standpoint. This method searches the optimal maintenance weeks for each generation unit, considering the objectives of both GenCo and ISO, simultaneously. The proposed MO-GMS model is formulated as a mixed-integer non-linear programming problem and examined on the IEEE 24-bus and IEEE 118-bus test systems. The success of the proposed multi-objective model is validated by comparing the obtained results with intelligent optimisation algorithms.

Loss-Minimizing Generation Unit and Tie-Line Scheduling for Asynchronous Interconnection

Asynchronous interconnection of two power systems by high voltage direct current transmissions (HVDCs) is a judicious choice to enhance the reliability of system operation and evade some risks. As one of the pioneers of asynchronous interconnection, the southern China power system encounters the challenge of coordinating generation of the sending-end and demand of the receiving-end. The day-ahead generation unit and tie-line scheduling problem is formulated as a two-stage mathematical optimization, in which the first-stage problem minimizes the variance of the remaining load series of receiving-end and generates a near-optimal interarea power transmission schedule, whereas the second-stage problem decides the production level of generation units and power flow of HVDC tie-lines with the objective of minimizing transmission losses. In the second-stage problem, discrete characteristics of HVDC power profile is explicitly modeled, and a quadratic loss function derived from historical data is used to calculate the overall loss of each HVDC, which is then piecewise linearized to form a tractable mixed integer linear programming. Numeric experiments based on realistic data of China Southern Power Grid are carried out. The feasibility and effectiveness of the proposed method is verified through comparing the optimized generation and transmission schedules with original ones which are generated based on expert knowledge.

Procuring and pricing performance‐based frequency regulation services in the electricity market

This study presents a market model that procures energy and performance-based regulation services simultaneously considering the participation of energy storage devices. The correlations of energy, regulation capacity, and regulation mileage are explicitly demonstrated. The proposed market model determines the energy schedule of generation units, charging and discharging profiles of energy storage devices, and the schedule of regulation services. Market clearing prices for energy, regulation capacity, and regulation mileage are derived and decomposed through Lagrange multiplier analysis. The relationships between the clearing prices of different market products are analysed. The proposed market model is tested and verified with the IEEE 30-bus system.

Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration

Day-ahead scheduling of generation units and storage devices is essential for the economic and efficient operation of a power system. Conventionally, a control center calculates the dispatch schedule by gathering information from all of the devices. However, this centralized control structure makes the system vulnerable to single point of failure and communication failures, and raises privacy concerns. In this paper, a fully distributed algorithm is proposed to find the optimal dispatch schedule for a smart grid with renewable and energy storage integration. The algorithm considers modified dc power flow constraints, branch energy losses, and energy storage charging and discharging efficiencies. In this algorithm, each bus of the system is modeled as an agent. By solely exchanging information with its neighbors, the optimal dispatch schedule of the conventional generators and energy storage can be achieved in an iterative manner. The effectiveness of the algorithm is demonstrated through several representative case studies.