A locational Marginal Price-Based Partition Optimal Economic Dispatch Model of Multi-Energy Systems

The large-scale penetration of renewable energy and deeply integrated multiple energy forms pose a major challenge for the accounting and allocation process of additional adjustment cost in multi-energy system and electricity market. The variable wind power and the operating status of multi-energy systems faced by the electricity market have driven researchers towards the decision of electricity spot market with complicated pricing mechanism. This paper presents a locational marginal price-based partition optimal economic dispatch model of multi-energy system with a high proportion of renewable energy. First, a model of the additional adjustment cost of multi-energy adjustable units to improve the power grid adjustment capacity is studied based on the power balance model of multi-energy conversion and storage. In the second, to improve the effectiveness of the ESM mechanism, an optimal economic dispatch model is established, with the goal of minimizing the additional adjustment cost of multi-energy adjustable units. Moreover, according to the characteristics of the model and the mechanism of locational marginal price-based economic dispatch, a solution method based on lagrangian relaxation is proposed. The incentive compatible locational marginal price is obtained. Finally, a simulation model of multi-energy systems based on IEEE 39-nodes power system and 7-nodes natural gas system is established. And the model is based on operating data of an actual grid with high proportion of renewable energy in Northeast China. The results of the examples show that the proposed method can effectively improve the efficiency and flexibility of multi-energy system. Another advantage of the proposed method is that the capabilities of the complementary multi-energy coordination can be promoted.

Two-stage stochastic integrated adjustment deviations and consensus models in an asymmetric costs context

Decision-makers usually have a variety of unsure situations in the environment of group decision-making. In this paper, we resolve this difficulty by constructing two-stage stochastic integrated adjustment deviations and consensus models (iADCMs). By introducing the minimum cost consensus models (MCCMs) with costs direction constraints and stochastic programming, we develop three types of iADCMs with an uncertainty of asymmetric costs and initial opinions. The factors of directional constraints, compromise limits and free adjustment thresholds previously thought to affect consensus separately are considered in the proposed models. Different from the previous consensus models, the resulting iADCMs are solved by designing an appropriate L-shaped algorithm. On the application in the negotiations on Grains to Green Programs (GTGP) in China, the proposed models are demonstrated to be more robust. The proposed iADCMs are compared to the MCCMs in an asymmetric costs context. The contrasting outcomes show that the two-stage stochastic iADCMs with no-cost threshold have the smallest total costs. Moreover, based on the case study, we give a sensitivity analysis of the uncertainty of asymmetric adjustment cost. Finally, conclusion and future research prospects are provided.

A Synthetic Approach for Datacenter Power Consumption Regulation towards Specific Targets in Smart Grid Environment

With the large-scale grid connection of renewable energy sources, the frequency stability problem of the power system has become increasingly prominent. At the same time, the development of cloud computing and its applications has attracted people’s attention to the high energy consumption characteristics of datacenters. Therefore, it was proposed to use the characteristics of the high power consumption and high flexibility of datacenters to respond to the demand response signal of the smart grid to maintain the stability of the power system. Specifically, this paper establishes a synthetic model that integrates multiple methods to precisely control and regulate the power consumption of the datacenter while minimizing the total adjustment cost. First, according to the overall characteristics of the datacenter, the power consumption models of servers and cooling systems were established. Secondly, by controlling the temperature, different kinds of energy storage devices, load characteristics and server characteristics, the working process of various regulation methods and the corresponding adjustment cost models were obtained. Then, the cost and penalty of each power regulation method were incorporated. Finally, the proposed dynamic synthetic approach was used to achieve the goal of accurately adjusting the power consumption of the datacenter with least adjustment cost. Through comparative analysis of evaluation experiment results, it can be observed that the proposed approach can better regulate the power consumption of the datacenter with lower adjustment cost than other alternative methods.

Primal Analytical Foundations of Dynamic Production Analysis

This chapter develops dynamic production analysis within the context of the adjustment cost model of the firm, where adjustment costs are associated with changes in the level of the quasi-fixed factors, also known as internal adjustment costs. The chapter characterizes axiomatically several primal representations of the adjustment cost production technology. The axiomatic approach is a cornerstone to model production technology both in theoretical and empirical work. The existence of several representations of the adjustment cost production technology is essential in the analysis of the firm’s decisions and its adjustment path that are conditioned by the technology. Three set representations of the adjustment cost production technology are discussed and characterized axiomatically. Two functional representations of the technology are also addressed using an axiomatic approach.

Dynamic Decision Making, Distance Functions, and Productive Efficiency

This chapter discusses three concepts of the directional distance function in the presence of internal adjustment costs, designated as adjustment cost directional distance functions. These functions are the building blocks of technical inefficiency measures. Duality between an adjustment cost directional distance function and an indirect optimal value function allows the construction of economic measures of inefficiency. Duality is established between the adjustment cost directional input function and the optimal current value function of the intertemporal cost minimization problem. From this dual relation, a dynamic cost inefficiency measure is derived and decomposed into technical inefficiency and allocative inefficiency. Similarly, dynamic input-output measures of inefficiency are derived from the adjustment cost directional technology distance function and duality between this function and the current profit function.