Participation of Grid-Connected Energy Hubs and Energy Distribution Companies in the Day-Ahead Energy Wholesale and Retail Markets Constrained to Network Operation Indices

Abstract

In this paper, the optimal scheduling of energy grids and networked energy hubs based on their participation in the day-ahead energy wholesale and retail markets is presented. The problem is formulated as a bilevel model. Its upper level minimizes the expected energy cost of electricity, gas, and heating grids, especially in the form of private distribution companies in the mentioned markets, in the first objective function, and it minimizes the expected energy loss of these networks in the second objective function. This problem is constrained by linearized optimal power flow equations. The lower-level formulation minimizes the expected energy cost of hubs (equal to the difference between sell and purchase of energy) as an objective function in the retail market. Constraints of this model are the operation formulation of sources and active loads and the flexibility limit of hubs. The unscented transformation approach models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage. Then, the Karush–Kuhn–Tucker approach and Pareto optimization technique based on ε-constraint are adopted to extract the single-level single-objective formulation. Finally, obtained results verify the capability of the present method in improving the economic status of hubs and the economic and operation situation of the mentioned networks simultaneously so that the proposed scheme by managing the power of energy hubs compared with power flow studies has been able to reduce operating costs by 8%, reduce energy losses by 10%, and improve voltage profile and temperature by 36% and 30%.

Fuzzy Decision-Based Energy Management of Energy Grids with Hubs considering Participation of Hubs and Networks in the Energy Markets

“…Also, + and − signs for active, thermal, and gas powers denote the performance model of the supply and demand of EHs for the said powers. Constraints ((25))-(( 28)) provide the CHP operating model [54,55]. Equations ( 25) and (26) give the thermal and gas power of CHP, which depend on active power of the CHP.…”

“…Then, the limit on apparent power flowing through the storage charger has a model similar to ((34)). Relationships ((31))-((34)) are true for mobile storage devices such as electric vehicles (EVs) [2,5,55]. Nevertheless, the number and type of EVs connected to EH are different in each operating hour [57,58].…”

“…International Journal of Energy Research the parameters χ CH and χ D , E I and E, and S E will have a subscript h. The values of χ CH , χ D , and S E at each instant are, respectively, equal to the sum of the charging rate, discharge rate, and charging capacity of EVs connected to EH, and E I at each moment can be found by summing up the initial energy of newly connected vehicles to the EH, and E is the sum of final energy of vehicles disconnected from EH [2]. In this article, it is assumed that each of the EVs fully charges its battery [5,55]. In the following, the apparent power limit of the renewable source is formulated in constraint (( 35)) [2].…”

“…A sample system with electricity, gas, and heat networks is tested by the suggested scheme (refer to Figure 2). The electricity network has 9 buses, while the gas and thermal networks include 4 and 7 nodes, respectively [15,55]. The base power of the electricity network is set at 1 MVA, while it is 1 MW for the gas and thermal networks [14].…”

“…The only consumers in the gas network are the CHP and boiler, and there is no passive gas demand. The hourly demand can be calculated by multiplying the peak load and load factor [13,55]. The daily load factor curves of electricity and thermal networks are available in [14].…”