ترغب بنشر مسار تعليمي؟ اضغط هنا

A Multi-Period Market Design for Markets with Intertemporal Constraints

136   0   0.0 ( 0 )
 نشر من قبل Jinye Zhao
 تاريخ النشر 2018
  مجال البحث
والبحث باللغة English




اسأل ChatGPT حول البحث

The participation of renewable, energy storage, and resources with limited fuel inventory in electricity markets has created the need for optimal scheduling and pricing across multiple market intervals for resources with intertemporal constraints. In this paper, a new multi-period market model is proposed to enhance the efficiency of markets with such type of resources. It is also the first market design that links a forward market and a spot market through the coordination of schedule and price under the multi-period paradigm, achieving reliability, economic efficiency and dispatch-following incentives simultaneously. The forward market solves a multi-period model with a long look-ahead time horizon whereas the spot market solves a series of multi-period dispatch and pricing problems with a shorter look-ahead time horizon on a rolling basis. By using the forward schedules and opportunity costs of intertemporal constraints as a guideline, the spot market model is able to produce economically efficient dispatch solutions as well as prices that incentivize dispatch following under the perfect forecast condition. The proposed scheme is applied to the dispatch and pricing of energy storage resources. Numerical experiments show that the proposed scheme outperforms the traditional myopic method in terms of economic efficiency, dispatch following and reliability.

قيم البحث

اقرأ أيضاً

In recent years, developments in plug in hybrid electric vehicles have provided various environmental and economic advantages. In the future smart grids, electric vehicles are seen as an important means of transportation to reduce greenhouse gas emis sions. One of the main issues regarding to this sort of vehicles is managing their charging time to prevent high peak loads over time. Deploying advanced metering and automatic chargers can be a practical way not only for the vehicle owners to manage their energy consumption, but also for the utilities to manage the electricity load during the day by shifting the charging loads to the off-peak periods. Additionally, an efficient charging schedule can reduce the users electricity bill cost. In this paper we propose a new coordinated multi-period management model for electric vehicles charging scheduling based on multi-objective approach, aiming at optimizing customers charging cost. In the proposed method, a stochastic model is given for starting time of charging, which makes the method a practical tool for simulating the vehicle owners charging behavior effectively. In order to verify the effectiveness of the proposed method, two market policies are used as case studies. The computation results can be used to evaluate the impact of electric vehicles scheduling on economic performance of smart grid.
Recent innovations in Information and Communication Technologies (ICT) provide new opportunities and challenges for integration of distributed energy resources (DERs) into the energy supply system as active market players. By increasing integration o f DERs, novel market platform should be designed for these new market players. The designed electricity market should maximize market surplus for consumers and suppliers and provide correct incentives for them to join the market and follow market rules. In this paper, a feeder-based market is proposed for local energy trading among prosumers and consumers in the distribution system. In this market, market players are allowed to share energy with other players in the local market and with neighborhood areas. A Two-StepMarket Clearing (2SMC) mechanism is proposed for market clearing, in which in the first step, each local market is cleared independently to determine the market clearing price and in the second step, players can trade energy with neighborhood areas. In comparison to a centralized market, the proposed method is scalable and reduces computation overheads, because instead of clearing market for a large number of players, the market is cleared for a fewer number of players. Also, by applying distributed method and Lagrangian multipliers for market clearing, there is no need for a central computation centre and private information of market players. Case studies demonstrate the efficiency and effectiveness of the proposed market clearing method in increasing social welfare and reducing computation time.
We study an intertemporal consumption and portfolio choice problem under Knightian uncertainty in which agents preferences exhibit local intertemporal substitution. We also allow for market frictions in the sense that the pricing functional is nonlin ear. We prove existence and uniqueness of the optimal consumption plan, and we derive a set of sufficient first-order conditions for optimality. With the help of a backward equation, we are able to determine the structure of optimal consumption plans. We obtain explicit solutions in a stationary setting in which the financial market has different risk premia for short and long positions.
Recent developments in urbanization and e-commerce have pushed businesses to deploy efficient systems to decrease their supply chain cost. Vendor Managed Inventory (VMI) is one of the most widely used strategies to effectively manage supply chains wi th multiple parties. VMI implementation asks for solving the Inventory Routing Problem (IRP). This study considers a multi-product multi-period inventory routing problem, including a supplier, set of customers, and a fleet of heterogeneous vehicles. Due to the complex nature of the IRP, we developed a Modified Adaptive Genetic Algorithm (MAGA) to solve a variety of instances efficiently. As a benchmark, we considered the results obtained by Cplex software and an efficient heuristic from the literature. Through extensive computational experiments on a set of randomly generated instances, and using different metrics, we show that our approach distinctly outperforms the other two methods.
This work develops a proximal primal-dual decentralized strategy for multi-agent optimization problems that involve multiple coupled affine constraints, where each constraint may involve only a subset of the agents. The constraints are generally spar se, meaning that only a small subset of the agents are involved in them. This scenario arises in many applications including decentralized control formulations, resource allocation problems, and smart grids. Traditional decentralized solutions tend to ignore the structure of the constraints and lead to degraded performance. We instead develop a decentralized solution that exploits the sparsity structure. Under constant step-size learning, the asymptotic convergence of the proposed algorithm is established in the presence of non-smooth terms, and it occurs at a linear rate in the smooth case. We also examine how the performance of the algorithm is influenced by the sparsity of the constraints. Simulations illustrate the superior performance of the proposed strategy.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا