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.

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.

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.

Institutional Commitment Problems and Regional Autonomy: The Catalan Case

This article examines what constitutional arrangements are more likely to facilitate the transfer of effective decision-making power to the regional level. We show that certain constitutional arrangements can result in institutional commitment problems between regional minority and national majority groups, which in turn influence levels of regional autonomy across regions. Specifically, we examine how the depth and scope of decentralization depend on the presence of federal agreements and the availability of institutional guarantees that make the federal contracts credible. Analyzing regional-level data, we show that regions where identity minority groups are majoritarian enjoy more regional autonomy when the commitment problem has resulted in a satisfactory national accommodation. Our findings highlight two important scenarios. The first occurs when the institutional commitment problem is solved, and regional minority groups are granted substantial levels of regional autonomy. The second scenario takes place when the commitment problem is not institutionally accommodated, and hence regional minority groups have systematically lower levels of autonomy. This article illustrates that both federal contracts and credible agreements are important tools to understand regional decision-making powers.

The Credible Commitment Problem in the Third-Party Mediation of the Mindanao Peace Process, 1975–2014

Negotiated settlements of civil wars are challenging since incompatibilities take a long time to resolve. Many scholars have approached this puzzle by identifying information asymmetry and commitment problems as critical deterrents to resolution. Similarly, this article argues that third-party mediation could improve or worsen the parties’ credible commitment problems, as illustrated in the Mindanao peace process mediation that spanned almost four decades. Following a contingency framework in analyzing third-party mediation, this article analyzes existing reports, statements, and peace process agreements using a process tracing methodology. The article argues that the success of a peace process could be attributed to how mediation resolves the parties’ credible commitment problems, which are evident in three aspects of the peace process: getting the parties to negotiate, the use of mediator leverage, and the promise of third-party monitoring and enforcement.

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.