The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.

Analysis of infeasible unit-commitment solutions arising in energy optimization

The day-ahead problem of finding optimal dispatch and prices for the Brazilian power system is modeled as a mixed-integer problem, with nonconvexities related to fixed costs and minimal generation requirements for some thermal power plants. The computational tool DESSEM is currently run by the independent system operator, to define the dispatch for the next day in the whole country. DESSEM also computes marginal costs of operation that CCEE, the trading chamber, uses to determine the hourly prices for energy commercialization. The respective models sometimes produce an infeasible output. This work analyzes theoretically those infeasibilities, and proposes a prioritization to progressively resolve the constraint violation, in a manner that is sound from the practical point of view. Pros and cons of different mathematical formulations are analyzed. Special attention is put on robustness of the model, when the optimality requirements for the unit-commitment problem vary.

Optimal Management and Control of Smart Thermal-Energy Grids

This work deals with the development of novel algorithms and methodologies for the optimal management and control of thermal and electrical energy units operating in a networked configuration. The aim of the work is to foster the creation of a smart thermal-energy grid (smart-TEG), by providing supporting tools for the modeling of subsystems and their optimal control and coordination. A hierarchical scheme is proposed to optimally address the management and control issues of the smart-TEG. Different methods are adopted to deal with the features of the specific generation units involved, e.g., multi-rate MPC approaches, or linear parameter-varying strategies for managing subsystem nonlinearity. An advanced scheme based on ensemble model is also conceived for a network of homogeneous units operating in parallel. Moreover, a distributed optimization algorithm for the high-level unit commitment problem is proposed to provide a robust, flexible and scalable scheme.

Optimal Power Dispatch in Energy Systems Considering Grid Constraints

As a consequence of the increasing share of renewable energies and sector coupling technologies, new approaches are needed for the study, planning, and control of modern energy systems. Such new structures may add extra stress to the electric grid, as is the case with heat pumps and electrical vehicles. Therefore, the optimal performance of the system must be estimated considering the constraints imposed by the different sectors. In this research, an energy system dispatch optimization model is employed. It includes an iterative approach for generating grid constraints, which is decoupled from the linear unit commitment problem. The dispatch of all energy carriers in the system is optimized while considering the physical electrical grid limits. From the considered scenarios, it was found that in a typical German neighborhood with 150 households, a PV penetration of ∼5 kWp per household can lead to curtailment of ∼60 MWh per year due to line loading. Furthermore, the proposed method eliminates grid violations due to the addition of new sectors and reduces the energy curtailment up to 45%. With the optimization of the heat pump operation, an increase of 7% of the self-consumption was achieved with similar results for the combination of battery systems and electrical vehicles. In conclusion, a safe and optimal operation of a complex energy system is fulfilled. Efficient control strategies and more accurate plant sizing could be derived from this work.

Hydrothermal Unit-Commitment Problem of a Large-Scale System with Representation of Forbidden Zones

As we move towards electrical networks with a growing presence of renewable generation, the representation of the electrical components becomes more important. In hydro-dominated power systems, modelling the forbidden zones of hydro plants becomes increasingly challenging as the number of plants increases. Such zones are ranges of generation that either should be avoided or are altogether unreachable. However, because representing the forbidden zones introduces a substantial computational burden, hydrothermal unit-commitment problems (HTUC) for large systems are usually formulated ignoring the forbidden zones. Nonetheless, this simplification may demand adjustments to the solution of the HTUC, because the generation of the hydro stations may fall in forbidden zones. In practice, the adjustments are usually performed based on the experience of system operators and, then, can be far from an optimal correction. In this paper, we study the impact of explicitly representing the hydro-generation forbidden zones in a large-scale system with more than 7000 buses, 10,000 lines, and 700 hydro units. Our findings show that the simplified model that is current used can deviate significantly from the model with forbidden zones, both in terms of the generation of hydro plants, as well as the generation of thermal plants and the system marginal costs.

A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stochastic, and only the 10-unit system is studied. The proposed technique outperforms the standard EO with cost savings of 40.93 USD. The simulation results demonstrate that MPSO-EO has fairly good performance for solving the UCP with significant total operating cost savings compared to standard EO compared with other reported techniques.

Impact of demand response programs in unit-commitment problems in energy optimization

Demand response is currently being tested by the Brazilian independent system operator, ONS, and by the trading chamber, CCEE. The program considers the reduction of consumption of some registered clients, as an alternative to dispatching thermal power plants according to merit order. The DESSEM computational tool, developed by CEPEL, is currently run by the ONS to define the next-day dispatch for the whole country. The results obtained using an academic version of DESSEM are used to benchmark and compare DESSEM's performance to relocate the load of demand offered by the operator to different clients under different configurations of the power system. Pros and cons are analyzed for different mathematical formulations, particularly regarding their impact on prices, operating costs, and computational times. Special attention is paid to determining the robustness of the considered models for a variety of optimality requirements for solving the unit-commitment problem.

A Priority List-Based Binary Crow Search Algorithm for Unit Commitment Problem

Abstract-This paper proposes a novel hybrid technique that combines the priority list (PL) with the binary crow search algorithm (BCSA) for solving the unit commitment problem (UCP). Firstly, the PL method aims to sort the generating units in ascending order according to their average full load costs which are the total costs that are computed at the maximum generation outputs. Secondly, the BCSA is developed and employed to search for the optimal schedule of the generating units to face the next hourly demand with minimum total operating costs that are related to the optimal power generation schedule at certain loading level. BCSA is a new meta-heuristic optimizer, which is featured of the crow's intelligence. It has only two adjustable parameters that make its implementation very simple and easy compared to other optimization techniques. Its effectiveness and feasibility were confirmed by 4, 10, and 26-unit systems and the results are compared with those obtained by GA, PSO, APSO, and BDE. The simulation results demonstrate the capability of the proposed PLBCSA in solving the UC problem with good convergence rate compared with the previous methods in the literature. Around of 2-4% reduction in the total costs is achieved using the proposed PLBCSA for the 26-unit test system compared with GA, PSO and the implemented PL-BPSO solutions.