Distributed collaborative optimization for coupled transportation and power systems operation considering carbon emission and elastic travel demand

In order to achieve the goals of “emission peak” and “carbon neutrality”, this paper proposes a collaborative optimization method of renewable energy and energy storage capacity for the construction of carbon-free county distribution networks, considering the complementary characteristics of wind and solar energy. Considering the uncertainty of renewable energy output, a multi-scenario collaborative stochastic optimization model of renewable energy and energy storage planning is established with the zero-carbon vision of county distribution network. Aiming at maximizing the net benefit of the wind-solar-storage configuration in a zero-carbon energy supply county system, the model optimizes the proportion structure of wind and solar energy sources as well as the corresponding energy storage capacity, taking the zero-carbon emission constraints into account. For solving the problem, the collaborative optimization model is converted into a mixed-integer linear programming model. Focusing on a county distribution grid in northern China, the impact of energy storage configuration and cascade utilization on the capacity configuration of various resources and the economic performance of the investment scheme in the zero-carbon wind-solar-storage energy supply system are analyzed respectively in case studies.

The optimal power flow (OPF) problem is fundamental in power system\noperations and planning. Large-scale renewable penetration in distribution\nnetworks calls for real-time feedback control, and hence the need for fast and\ndistributed solutions for OPF. This is difficult because OPF is nonconvex and\nKirchhoff's laws are global. In this paper we propose a solution for balanced\nradial distribution networks. It exploits recent results that suggest solving\nfor a globally optimal solution of OPF over a radial network through the\nsecond-order cone program (SOCP) relaxation. Our distributed algorithm is based\non alternating direction method of multiplier (ADMM), but unlike standard ADMM\nalgorithms that often require iteratively solving optimization subproblems in\neach ADMM iteration, our decomposition allows us to derive closed form\nsolutions for these subproblems, greatly speeding up each ADMM iteration. We\npresent simulations on a real-world 2,065-bus distribution network to\nillustrate the scalability and optimality of the proposed algorithm.\n

The optimal power flow (OPF) problem is fundamental in power system operations and planning. Large-scale renewable penetration calls for real-time feedback control, and hence the need for fast and distributed solutions for OPF. This is difficult because OPF is nonconvex and Kirchhoff's laws are global. In this paper we propose a solution for radial networks. It exploits recent results that suggest solving for a globally optimal solution of OPF over a radial network through the second-order cone program (SOCP) relaxation. Our distributed algorithm is based on alternating direction method of multiplier (ADMM), but unlike standard ADMM algorithms that often require iteratively solving optimization subproblems in each ADMM iteration, our decomposition allows us to derive closed form solutions for these subproblems, greatly speeding up each ADMM iteration. We present simulations on a real-world 2,065-bus distribution network to illustrate the scalability and optimality of the proposed algorithm.

In this paper, we reviewed several forms of alternating direction method of multipliers (ADMM) for distributed power system computing. The major focus is on ADMM based distributed parallel optimization algorithm which is feasible to be implemented in power network. Firstly, we introduced the general form of ADMM, and extend the 2-block ADMM to N-block multi-block ADMM. Next, we focus on two distributed parallel ADMM based optimization algorithms: Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM). A three-area DC optimal power flow (OPF) problem and a two-area AC OPF problem are tested for ADMM implementation. Information exchange structure and the numerical convergence results of the ADMM algorithms are given.

With the increasing penetration of electric vehicles in modern cities, the tie between urban transportation system and power distribution system is becoming more evident. Spatial and temporal distributions of load demands in fast charging stations alter power flow patterns, raising the risk of over-low bus voltage and congestions, and thus threaten distribution system security. Similar to the flexible demand in smart grid, traffic demand in the transportation system, which is defined as the volume of vehicles travelling from one place to another in unit time, is also shiftable over time, as motorists would like to avoid rush hours and save travel time. This paper proposed a multi-period traffic assignment model with time-shiftable traffic demands. In order to capture the deliberate postponed or advanced travels, the dynamic traffic demand between each origin-destination pair nodes over time is formulated as a matrix variable. The traffic assignment model consists of several Wardrop user equilibrium problems in consecutive periods coupled by demand conservation constraints, and gives rise to a convex program with polyhedral constraints. The charging station loads are assumed to be proportional to the traffic flow in the service area. The power distribution system is operated in accordance with optimal power flow. Case studies demonstrate the benefit brought by the temporal flexibility of traffic demand.

This paper proposes a convex optimization based distributed algorithm to solve the multi-period optimal gas- power flow (OGPF) problem in coupled energy distribution systems. At the gas distribution system side, the non- convex Weymouth gas flow equations are convexified as quadratic constraints. Then the optimal gas flow (OGF) subproblem is solved by an iterative second-order cone programming (SOCP) procedure, whose efficiency is two orders of magnitudes higher than traditional nonlinear methods. A convex quadratic program based initiation scheme is suggested, which helps to find a high-quality starting point. At the power distribution system side, convex relaxation is performed on the non-convex branch flow equations, and the optimal power flow (OPF) subproblem gives rise to an SOCP. Tightness is guaranteed by the radial topology. In the proposed distributed algorithm, the OGF problem and the OPF problem are solved independently, and coordinated by the alternating direction multiplier method (ADMM). Numerical results corroborate significant enhancements on computational robustness and efficiency compared with existing OGPF calculation methods.

The collaborative development of conventional buses and urban metro has become an important research topic for the priority development of urban public transport. The topic of collaborative optimization of feeder bus route design and operation is studied in this study. The objective function is to minimize the total travel time of passengers and the operation cost of feeder buses. The improved particle swarm optimization (PSO) algorithm is used to solve the collaborative optimization model, and the effectiveness of the model and algorithm is verified through the case study. The research shows that it is feasible in model construction and algorithm to carry out collaborative optimization of feeder bus route design and operation. Compared with the multiple-to-one (M to 1) mode, the multiple-to-multiple (M to M) mode can better satisfy the needs of passengers from different places of departure and destinations to achieve a more reasonable and realistic goal. The case study is based on two metro stations and 16 feeder bus stops on Fuzhou Metro line 2 to obtain two bus routes and a corresponding operation scheme. Under the same topology road network, the operation time of the improved PSO algorithm is much shorter than the DFS algorithm, the total cost error of the feeder bus is 0.04%, and the departure frequency error is 4.6%, which is within the reasonable error range. Therefore, the collaborative optimization model proposed in this study is feasible and effective in optimizing the feeder bus routes and operation.

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.

Global climate change is an international recognized issue and the most critical challenge of the 21st century for both developed and developing countries. Although many policy recommendations and fuel standards have already been taken in some developed countries to meet the long term goal which required the entire world to reduce carbon emissions by 50 percent by 2050, there are no strict standards and generally accepted guidelines of low carbon transportation strategic planning that have been developed. This paper analyzes and discusses current carbon emission situations and policies of urban transportation systems. The key technologies and strategy approaches that could be used to reduce carbon emissions of integrated transportation systems with the focuses on the following six areas: (1) Identification of Sources and Quantification of Carbon Emissions in Urban Transportation Systems (2) Development of Strategic Plan for Low Carbon Transportation Systems (3) Guidelines for the Development for Land Use and Urban Transportation Planning (4) Development of Balanced Multi-mode Transportation Systems (5) Methodologies and Guidelines for Megacity Travel Demand Management (6) Development of Comprehensive Systems for Evaluation of Low- Carbon Transportation System.

A collaborative optimization model is proposed to integrate the energy performance to the optimization of sintering burden. Firstly, the collaborative optimization problem in sintering burden is described, and then the association model between sinter raw material proportion, production parameter and energy consumption, drum index is provided using data-based methods. Furthermore, objective functions and constrains of the collaborative optimization model are generally formulated based on the association models. During the phase of model reduction and solving, linearization is conducted to the model, and production parameters are fixed to some typical value to investigate the model performance under these specific conditions. Case study derived from a large-scale iron and steel enterprise shows that the association models can demonstrate the relationship effectively. Meanwhile, Energy performance was optimized with the production performance satisfied.

High-performance tooth flank collaborative optimization model for spiral bevel and hypoid gears

Many different kinds of transportation systems such as ‘group rapid transit’, etc., have recently been presented as ideal urban public transportation systems. When planning to introduce such a new transportation system into a city, it is necessary to select the most desirable system for that city from those that have been proposed. The comprehensive evaluation of transportation systems must reflect various aspects such as the costs of construction and maintenance and the viewpoints or both the system users and the local inhabitants. This makes it one of the most difficult and yet vitally important problems in public transportation planning. The analytic hierarchy process (AHP) developed by T. L Saaty is straightforward and has the feature that it can deal with both qualitative and quantitative factors at the same time, and it is suitable for applying to complex evaluation problems, In this paper, three transportation systems are proposed for one of the newly planned towns in Kansai Cultural and Academic Research Complexes, and a comprehensive evaluation of these systems is performed by applying the AHP. It has been proved through the process of evaluation that information which is very useful in reaching a consensus for choosing a system can be obtained.

Penalized regression-based clustering model (PRClust) is an extension of “sum-of-norms” clustering model. Three previously proposed heuristic algorithms for solving PRClust are: (1) DC-CD, which combines the difference of convex programming (DC) and a coordinate-wise descent algorithm (CD), (2) DC-ADMM, which combines DC with the alternating direction method of multipliers (ADMM), and (3) ALT, which uses alternate optimization. DC-CD uses $$ p \times \left( {n \times \left( {n - 1} \right)} \right)/2 $$ scalar slack variables to solve PRClust, where n is the number of data and p is the number of their features. In each iteration of DC-CD, these slack variables and cluster centers are updated using a second-order cone programming (SOCP). DC-ADMM uses $$ p \times n \times \left( {n - 1} \right) $$ scalar slack variables. In each iteration of DC-ADMM, these slack variables and cluster centers are updated with a standard ADMM. In this paper, first, PRClust is reformulated into an equivalent model. Then, a novel heuristic algorithm is proposed to solve the reformulated model. Our proposed algorithm needs only $$ \left( {n \times \left( {n - 1} \right)} \right)/2 $$ scalar slack variables which are much less than those of DC-CD and DC-ADMM, and updates them using a simple equation in each iteration of the algorithm. Therefore, updating slack variables in our proposed algorithm is less time-consuming than that of DC-CD and DC-ADMM. Our proposed algorithm updates only cluster centers using an unconstrained convex quadratic problem. Therefore, our proposed unconstrained convex quadratic problem is much smaller than the SOCP of DC-CD which is used to update both cluster centers and slack variables. Meanwhile, ALT updates cluster centers using a SOCP, while our proposed algorithm updates cluster centers using an unconstrained convex quadratic problem with the same number of variables. Solving an unconstrained convex quadratic problem is less time-consuming than a SOCP with the same number of variables. Our experimental results on 12 datasets confirm that the runtime of our proposed algorithm is better than that of DC-ADMM and DC-CD.

The biggest carbon emissions in urban areas are generated from the transportation system, including in Surabaya City where the transportation sector contributes 5.48 million tons of carbon emissions per year, or about 96% of the total air emissions there. With the cities rapid development, urban transportation systems need to be managed appropriately to avoid environment damage. The most effective way to reduce carbon emissions from the transportation system is by reducing the number of private vehicles uses. TOD is one of the solutions that can be applied, which is through the integration of mixed land development and the construction of transit mode transportation systems. TOD allows people to carry out various daily activities with close transportation distances, either using transit modes, walking, or cycling. This study aims to show the benefits of applying TOD in reducing carbon emissions from the transportation sector in Surabaya using a system dynamics model. The model produced can be used as a reference or consideration for the government and other related parties in developing strategies and policies related to the implementation of TOD. Future studies can analyze several TOD scenarios that can be carried out to reduce carbon emissions from the transportation sector in Surabaya.

This paper presents a holistic modeling framework for the interdependent transportation network and power distribution network. From a system-level perspective, on-road fast charging stations would simultaneously impact vehicle routing in the transportation system and load flows in the distribution system, therefore tightly couple the two systems. In this paper, a dedicated traffic user equilibrium model is proposed to describe the steady-state distribution of traffic flows comprised of gasoline vehicles and electric vehicles. It encapsulates route selections, charging opportunities, electricity prices, and individual rationalities of minimum travel expense in a convex traffic assignment problem over an extended transportation network. An adaptive path generation oracle is suggested to solve the problem in a tractable manner. Economic operation of the power distribution system is formulated as an alternating current optimal power flow problem. Convex relaxation is performed. The optimal generation dispatch and nodal electricity prices can be computed from a second-order cone program. It is revealed that an equilibrium state will emerge due to the rational behaviors in the coupled systems, which is characterized via a fixed-point problem. A best-response decomposition algorithm is suggested to identify the network equilibrium through iteratively solving the traffic assignment problem and the optimal power flow problem, both of which entail convex optimization. Illustrative examples are presented to validate related concepts and methods.