Commitment Problem Research Articles

Nudging Payment Behavior: Evidence from a Field Experiment on Pay-as-You-Go Off-Grid Electricity

Abstract This paper reports results from a randomized control trial with a pay-as-you-go (PAYG) solar system provider in Pakistan. In the default treatment, customers are told the amount to pay every month to keep the system active. In a first treatment, customers are assisted in planning this monthly payment. A second treatment discloses that payments can be made flexibly within the month. This disclosure may reduce contract cancellation by helping minimize transaction costs but may increase contract complexity and reduce discipline. A third treatment combines flexibility with assistance in planning payments. Disclosing flexibility increases contract cancellation relative to the default, but combining flexibility with planning offsets this effect. Treatment effects appear stronger among users facing high mental constraints and transaction costs. These findings support the idea that behavioral factors, such as inattention and commitment problems, lay behind the negative impact of flexibility on cancellation. The results suggest that providers of PAYG systems may face a trade-off between disclosing complex contractual features and customer retention. Planning helps customers handle the added complexity.

동맹국의 정찰위성 보유가 확장억지에 미치는 영향

A revolution in military affairs(RMA) is expected to have a significant impact on militarized interstate disputes. Recent researches show that ballistic missiles, unmanned attack vehicles, and drones affect many aspects of militarized disputes. However, there is few research on the effect of the RMA on extended deterrence. In this study we analyze the effect of military surveillance technology on extended deterrence. In particular, we focus on the presence or absence of allies with reconnaissance satellites because reconnaissance satellite is a technology that can effectively alleviate uncertainty of information compared to other reconnaissance assets. Considering Fearon’s(1995) argument that wars occur because of uncertainty of information and commitment problem, reconnaissance satellites may be able to alleviate these problems with satellites characteristics such as high durability, stability, and few international legal restriction. Results of this study demonstrate that states having allies with reconnaissance satellites capabilities are less likely to be targeted in large scale militarized disputes with 100 or more casualties than states without having allies with reconnaissance satellites capabilities.

Economic Sanctions and the Escalation of Terrorism

Abstract A common belief is that sanctions will deprive targets of the resources they need to engage in offensive behaviors. This suggests that sanctions on active and passive supporters of terrorism should reduce violence. This study examines the argument that sanctions can weaken state support for terrorism and reduce terrorist violence. We argue that while sanctions may deprive active or passive supporters of resources, these market imperfections may create commitment problems by weakening these states’ ability to maintain the balance of power with their rivals. As a result, state supporters and their terrorists may escalate violence to solidify gains against their rivals. We test this hypothesis quantitatively using the Threat and Imposition of Economic Sanctions and the Global Terrorism datasets, and qualitatively using an examination of Pakistani support for the Haqqani Network.

Integrating Battery Energy Storage Systems in the Unit Commitment Problem: a Review

Integrating Battery Energy Storage Systems in the Unit Commitment Problem: a Review

A New Notion of Reserve for Power Systems With High Penetration of Storage and Flexible Demand

Modern power systems face important demand uncertainties due to increasing penetration of behind-the-meter renewable generation. System operators need to account for such uncertainties when solving the unit commitment and economic dispatch problem. The research literature has proposed advanced methods for decision making under uncertainty but, in practice, actual system operators put more trust in the tried-and-true approach of dealing with future uncertainty by committing reserves. In this paper, the unit commitment and economic dispatch problem is formulated for a system with high penetration of storage and the inadequacy of methods based on the traditional notion of reserves is exposed. Namely, in contrast to a generator, a storage unit can provide reserve capacity in a number of timeslots but it cannot provide an analogous reserve activation in all of those timeslots due to the battery's energy being depleted. After discussing two plausible but inadequate approaches, a new, generalized notion of reserves is proposed, which addresses these issues while not abandoning the practical, reserve-based approach for the operator's problem, thus making the best of both worlds. The proposed scheme enables storage units to provide reserves, without putting the system at risk of energy scarcity, which is shown to result in substantial cost savings.

More Efficient Energy Management for Networked Hybrid AC/DC Microgrids With Multivariable Nonlinear Conversion Losses

Accurate conversion loss models are the keys to guarantee the more efficient operation of networked hybrid ac/dc microgrids (N-ac/dc-MGs). A two-stage stochastic unit commitment (UC) problem is proposed to improve the operational efficiency of N-ac/dc-MGs under uncertain renewable energy generation output and loads. The nonlinear power losses of ac/dc converters and dc/dc converters under different operating modes are formulated as novel multivariate nonlinear functions of both power and voltage. These functions are approximated by linear surrogate models and adopted by the dynamic optimal power flow (OPF), where the voltage and power of the converters can be optimized considering the physical laws among voltages of converters. Embedding the dynamic OPF problem as mixed-integer recourse, a two-stage stochastic programming problem is formulated for the day-ahead operation of N-ac/dc-MGs. To reduce the computational cost, a finite iteration convergent Benders decomposition algorithm is proposed to solve the UC problem. Case studies are performed on hybrid ac/dc MGs and N-ac/dc-MGs. Simulation results reveal that using more accurate conversion loss models, the output commands, especially the dc bus voltage, from the energy management systems will lead to more efficient system operation, and thus, save energy.

PERAN KEPUASAN KERJA DALAM MEMEDIASI PENGARUH SERVANT LEADERSHIP TERHADAP KOMITMEN ORGANISASIONAL PEGAWAI BPKAD KABUPATEN GIANYAR

The background of this research is the existence of organizational commitment problems that occur in employees. The purpose of this study was to analyze the influence of servant leadership on organizational commitment with job satisfaction as a mediation for employees of the Badan Pengelolaan Keuangan dan Aset Daerah Kabupaten Gianyar. The sample used is as many as 130 people who are determined through probability sampling technique with simple random sampling, which is taken randomly. Data was collected through interviews and questionnaires. The analytical technique used is a path analysis, sobel test, and VAF (Variance Accounted For) test. The results of this study indicate that servant leadership has a positive and significant effect on organizational commitment. Job satisfaction has a positive and significant effect on organizational commitment. Servant leadership has a positive and significant effect on job satisfaction. Job satisfaction mediates the influence of servant leadership on organizational commitment. The implications of the results of this study are expected to provide an understanding that when wanting to increase organizational commitment, the organization should be able to improve servant leadership, job satisfaction to its employees.

Short-Term Load Forecasting Based on Bayesian Ridge Regression Coupled with an Optimal Feature Selection Technique

Load forecasting has been an important aspect in power system operations. The increase in the integration of different renewable energy resources during past decades has made it even more crucial as an accurate load forecast can be highly beneficial for the energy market as well as the ongoing economic dispatch and unit commitment problem. The increased influence of artificial intelligence and machine learning in electrical engineering has also caused an improvement in load forecasts immensely. This study presents a short-term load forecasting methodology using Bayesian Ridge Regression paired up with an optimal feature selection technique which is a combination of Coyote Optimization Algorithm and Quadratic Discriminant Analysis. The test systems used in the study are based on the historical load data obtained from publicly available API offered by PJM data miner 2 and the weather data obtained using Visual Crossing. Before the application of the feature selection technique, the features were engineered by lagging the weather and the load data. The results of this method are compared with multiple state-of-the-art load forecasting methods including Gradient Boosting Regressor, Random Forest Regressor, Ensemble from ElasticNet, and Bagging with Decision Tree. The proposed method proved to be superior as it showed a noticeable decrease in mean absolute percentage error and root-mean-square error.

Stronger Challengers can Cause More (or Less) Conflict and Institutional Reform

Prominent theories propose that commitment problems drive phenomena such as war and democratization. However, existing work disagrees about a basic question: how does a challenger’s coercive strength affect prospects for conflict and/or institutional reform? We establish that the relationship depends on how challenger strength affects the average and maximum probability of winning a conflict in a given period (“threat”). We analyze a formal model with a general distribution of threats, and conceptualize challenger strength as affecting this distribution. If the maximum threat is fixed and stronger challengers pose a higher average threat, then weak challengers will rebel (absent reform) during the rare periods they pose a high threat. However, if stronger challengers pose a greater maximum threat, then they are harder to buy off. Applying these insights advances theoretical and empirical debates about democratization.

Caring is sharing: Why independent commissions in post-conflict societies have power-sharing arrangements

The inclusion of conflict parties in independent commissions through power-sharing has been found to reduce the reoccurrence of conflict. Yet, the theoretical and empirical literature explaining why independent commissions include power-sharing is very limited. Previous publications have focused on in-depth case studies that explain how power-sharing prevents conflict recurrence in specific post-conflict societies but do not provide a general argument or widescale testing beyond individual case studies. This article provides a new systematic, general theoretical argument and novel empirical testing that explains why there is power-sharing on some commissions but not others. We argue that conflict parties adopt power-sharing provisions in independent commissions because doing so allows them to overcome significant credible commitments problems that are inherent to the ending of intrastate conflict. Using a new and comprehensive dataset, Independent Commissions in Post-Conflict Societies, which includes information on 580 commissions (1990–2016), this article applies a combination of decision trees and regression analysis to test our hypotheses. The findings indicate that power-sharing is adopted where credible commitment problems are acute and show that commissions working on political or security issues and those with monitoring or verification roles, or that work on the implementation of peace agreements, are more likely to include power-sharing arrangements.

Adaptive distributed scheduling of the resources in smart grids with considering demand response

Unit commitment (UC), a non-linear and non-convex problem, is one of the important problems in power system operation. Solving the UC problem centrally has problems such as security concerns. On the contrary, solving it in a fully distributed manner reduces performance speed. This paper presents a method for solving the UC problem that addresses both security concerns and speeds up performance. This paper intends to solve the UC problem by using a hybrid structure and with the help of accelerator and cyber security parameters of the power system. This problem is solved for the first time in a power system despite its non-linear nature and non-convexity. Also, the demand response (DR) issue is considered as a contributing factor in resource scheduling management. This is important because DR is an inherently distributed issue. The presented method uses a developed decentralized algorithm called the accelerated hybrid alternating direction method of multipliers (AHADMM). AHADMM is a combination of the ADMM framework and hybrid network architecture that its parameters are tuned. Then for decentralized optimization, the generalized Benders decomposition (GBD) algorithm and the AHADMM algorithm are utilized to speed up the computation and provide a framework to exploit the distributed nature of the problem. The proposed algorithm consists of two main problems, primal and master problems. The primal problem, which is solved in a distributed way, is responsible for providing a feasible solution such that an optimality cut can be generated. In contrast, the master problem is responsible for accumulating the optimality cuts and approximating the feasible region of the original non-linear problem. Regarding the improvement of the solution method, we have used the accelerator parameters and the limitation parameter of fusion centers in such a way as to guarantee the speed of calculations and the cyber security of the power system.

New Mixed-Integer Nonlinear Programming Formulations for the Unit Commitment Problems with Ramping Constraints

Mixed-Integer Formulations for Power Production Problems The unit commitment problem is a complex mixed-integer nonlinear program that originates in the field of power production. Although it arises in a monopolistic system, there is still great attention to this problem even in a free-market regime, where it constitutes only a subproblem of larger ones. Historically, it was usually solved by Lagrangian relaxation methods. However, the advances achieved by commercial solvers of mixed-integer (linear and convex) programming problems have made such approaches an attractive option. T. Bacci, A. Frangioni, C. Gentile, and K. Tavlaridis-Gyparakis present the first mixed-integer nonlinear programming formulation with a polynomial number of both variables and constraints that describes the convex hull of the feasible solutions of the unit commitment problem with a single thermal generation unit, comprising all typical constraints and convex power generation costs. Proving that the formulation is exact requires a new result about the convex envelope of specially structured functions that can have independent interest. This new formulation for a single power generation unit is used to derive three new formulations for the general unit commitment problem whose effectiveness has been tested against the state-of-art formulation.

Multi-objective two-stage stochastic unit commitment model for wind-integrated power systems: A compromise programming approach

In this paper, we introduce a compromise programming (CP) framework for solving a multi-objective two-stage stochastic unit commitment problem characterized by high penetration of wind power. The proposed framework aims at finding best-compromise Pareto efficient on/off schedules, accounting for wind and power demand uncertainties: such solutions must trade off the three objectives of operating cost, CO2 emissions, and wind power curtailment in accordance to the decision maker preferences. To achieve this, we introduce a practical procedure to compute the ideal and Nadir points associated to the multi-objective two-stage stochastic unit commitment problem and propose a linearized ℓ1 norm-based compromise program to design best-compromise on/off schedules that correspondingly minimize and maximize their weighted distances to the ideal point and to the Nadir point, considering the preference weights assigned by the decision maker to each of the three objective functions. The proposed CP framework is applied to a case study related to the New England IEEE-39 bus test system. The results show that, compared to the schedule obtained through the traditional minimization of operating cost, the designed best-compromised schedules considerably improve CO2 emissions reduction and wind power curtailment performance by conservatively sacrificing operating cost performance.

Wind curtailment mitigation in presence of battery energy storage and electric vehicle: A comprehensive multi-objective decision-support framework

Concern to maintain exploitation of power system in secure mode, the wind curtailment (WR) caused by the perpetual fluctuating of wind power (WP) can menace the operational power system schedule. The mentioned phenomenon will intensify by high WP penetration into the power system. In addition, incorporating plugged-in hybrid electric vehicles (PHEVs) into the power system along with the WP intermittent will increase the vagueness rating of the problem. Due to the expansion and development of battery energy storage (BES), the possibility of power shortage compensating and accumulating additional power produced by wind farms (WFs) has been engendered. Providing a stochastic scenario-based structure with different correlation levels can increase the robustness of the model. Here, a two-stage model has been proposed to overcome such a high-dimensioned problem. The first stage determined the scheduled power output of thermal generators (TGs) as the unit commitment (UC) problem to minimize operational costs. Next, by reckoning the supplementary constraints, the scheduled WP, which is re-optimized, will be obtained. The second stage has employed to detect the BES capacity. In this stage, the intelligent parking lots (IPLs) assign price-based signal impetus to the PHEVs to participate in economic dispatch. Due to the prominence of being equipped with the supreme decision-making facility for the intelligent decision-maker operator (IDMO), the augmented epsilon-constraint method has been employed to capture the Pareto sets of some multi-objective functions. The optimum solution has been selected by the decision-making method (DMM) according to the operator's preferences. The results of proposed Multi-Objective Decision-Support (MODS) model show that the total operation cost and BES sizing have dropped by 68 and 6.1 percent, respectively. The performance of the proposed model has been testified through the IEEE-30 bus system via GAMS software.

Novel Resolution of Unit Commitment Problems Through Quantum Surrogate Lagrangian Relaxation

Unit commitment (UC) problems faced by Independent System Operators on a daily basis are becoming increasingly complex due to the recent push for renewables and the consideration of sub-hourly UC to accommodate the increasing variability in the net load. A disruptive solution methodology to address the growing complexity is therefore required. Quantum computing offers a promise to overcome the combinatorial complexity through the use of the so-called “qubits.” To make the best use of quantum computers available currently or in the foreseeable future to solve UC problems with a much larger number of binary variables than the number of qubits available, this paper devises a novel solution methodology based on a synergistic combination of quantum computing and Surrogate Lagrangian Relaxation (SLR) to solve UC problems. Our new contributions include: 1) A Quantum-SLR (QSLR) algorithm incorporating quantum approximate optimization algorithm (QAOA) into the SLR method, which overcomes the fundamental difficulties of previous LR-based quantum methods such as zigzagging of multipliers and the need to know or estimate the optimal dual value for convergence; 2) A Distributed QSLR framework (D-QSLR) capable of coordinating local quantum/classical computing resources with those within neighborhoods and, in the meantime, protecting data privacy; 3) A Quantized UC model to obtain accurate commitment unit subproblems decision by using a quantum machine; and 4) A time-unit-decomposed quantum UC approach to overcoming the quantum resources’ limitations. Promising quantum test results validate the effectiveness of QSLR and the scalability of the UC-oriented D-QSLR algorithm, which demonstrate QSLR’s enormous potential in UC optimization.

Online Learning of Stable Integer Variables in Unit Commitment Using Internal Information

In a set of similar unit commitment (UC) problems, a variable is called a stable integer variable (SIV) if its optimal value is 0 or 1 on a regular basis, which reflects an inherent pattern in UC solutions. The computational complexity can be significantly reduced by fixing SIVs with a minor chance of accuracy loss. To identify SIVs, this letter proposes a method to collect internal solution information from the initial branch-and-bound process of artificial mixed-integer programming problems generated from the given problem. A distinct advantage of this method is that because no prior offline training is needed, learning is instance-specific and has no dependency on the training set. With identified SIVs, machine learning-based acceleration is achieved. Based on 50 test cases from publicly available and practical data, the proposed method improves the percentage of solved problems within the clearing time window from 68% to 96% compared with commercial solvers, counting the total time of data collection, prediction, and optimization.

The polytope of binary sequences with bounded variation

We investigate the problem of optimizing a linear objective function over the set of all binary vectors of length n with bounded variation, where the latter is defined as the number of pairs of consecutive entries with different value. This problem arises naturally in many applications, e.g., in unit commitment problems or when discretizing binary optimal control problems subject to a bounded total variation. We study two variants of the problem. In the first one, the variation of the binary vector is penalized in the objective function, while in the second one, it is bounded by a hard constraint. We show that the first variant is easy to deal with while the second variant turns out to be more complex, but still tractable. For the latter case, we present a complete polyhedral description of the convex hull of feasible solutions by facet-inducing inequalities and devise an exact linear-time separation algorithm. The proof of completeness also yields a new exact primal algorithm with a running time of O(nlogn), which is significantly faster than the straightforward dynamic programming approach. Finally, we devise a compact extended formulation.A preliminary version of this article has been published in the Proceedings of the 7th International Symposium on Combinatorial Optimization (ISCO 2022) (Buchheim and Hügging, 2022).

Multi-Stage Robust Dynamic Unit Commitment Based on Pre-Extended -Fast Robust Dual Dynamic Programming

To naturally characterize the long-time sequence coupled equipment such as energy storage systems (ESS) and to enforce the non-anticipativity of dispatch decisions under uncertain renewable energy, a multi-stage robust dynamic unit commitment (MRDUC) model is proposed in this paper. MRDUC transforms the multi-stage robust unit commitment into a dynamic programming form, which is the first attempt to combine day-ahead optimization with intra-day multi-stage robust dynamic dispatch. The unit commitment problem is solved in the pre-stage, and a simulation of intra-day multi-stage economic dispatch is done thereafter, which further refines the UC decision in an iterative way. A pre-extended -fast robust dual dynamic programming (PE-FRDDP) method is proposed to efficiently solve the MRDUC problem, which simultaneously optimizes the day-ahead UC and the intra-day decision policies. Case studies on the modified IEEE 6-Bus and 4 large-scale systems from 300 to 1888 buses demonstrate the effectiveness of the proposed model and the solution methodology.

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid data-driven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. The novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

An ultra-fast optimization algorithm for unit commitment based on neural branching

The computational efficiency of unit commitment (UC) is important for power system operations. Traditionally the unit commitment problem is solved per hour in a day, but with the scale of the power system and the electricity market continuing to expand, the large-scale UC problem will be hard to be solved within 1 h which will affect the power system operation and market clearing. To reduce the solving time of the large-scale UC problem, an ultra-fast optimization algorithm of neural branching for unit commitment (NBUC) is proposed. NBUC learns the branch and bound (B&B) decision made by full strong branching (FSB), which can generate the perfect B&B order to minimize the iterative process but take a lot of time to decide the perfect order by using graph convolutional neural network according to the historical data, and then makes the perfect order prediction with a certain precision without spending a lot of time to make B&B decision which minimizes the solving time including the iteration time and decision time in order to solve the large-scale UC problem quickly. A modified RTS-96 bus system is used to validate the effectiveness of the proposed NBUC. The results show 9.3% and 23.2% reductions in computational time compared with commercial software CPLEX and SCIP.