Optimal investment and operation of a microgrid to provide electricity and heat

Angarita, Jorge L.; Jafari, Hossein; Mohseni, Mojtaba; Al-Sumaiti, Ameena Saad; Heydarian‐Forushani, Ehsan; Kumar, Rajesh

doi:10.1049/rpg2.12190

Cited by 9 publications

(4 citation statements)

References 39 publications

(58 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation ( 22) refers to the bus voltage constraint. On the other hand, considering that one of the objectives of the capacity market is to cover the fixed costs of the generation units in order to maintain their motivation for generation expansion, the proposed price of consumers is limited by the Equation (23). The reason for this constraint is that their participation does not lead to a decrease in business prosperity in generation expansion.…”

Section: Other Constraints Of the Problemmentioning

confidence: 99%

“…The proposed adaptive approach combines the advantages of both the stochastic day‐ahead and the real‐time energy scheduling and reduces the real‐time operational cost of the microgrid. A robust investment and operation model has been proposed in [23] to attend to the power and heat needs of a microgrid connected to the distribution system. The optimization algorithm decides on the best investment and operation of combined heat and power, boilers, PV power generation and battery energy storage systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling of microgrids to insure resource adequacy in the capacity market

Katiraee

Jalili

2023

IET Renewable Power Gen

View full text Add to dashboard Cite

The penetration of renewable resources in distribution networks have led to the creation of a concept called microgrids. Microgrids act as a bus with the ability to control the amount of consumption and generation, from the viewpoint of system operator. If the amount of capacity that can be provided by microgrids is known, they can be effective in providing resource adequacy and the required capacity of the network. This paper contains two contributions. Modelling of capacity value of a microgrid that includes wind turbines, photovoltaic, non‐renewable generators, loads and batteries is the first innovation of this paper. Here, a capacity value model for microgrids is created for the first time. The application of microgrid capacity value model to a long‐term issue, such as ensuring resource adequacy in the capacity market, constitutes the second innovation of this paper. Consequently, there are two distinct innovations in this work. The presence of microgrids in the capacity market has increased competition and reduced costs, and on the other hand, it will help the microgrids development through their long‐term generation revenues. The proposed method has been tested on IEEE 57‐bus test system. Results have shown the efficiency of using microgrids in the capacity market.

show abstract

Section: Other Constraints Of the Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling of microgrids to insure resource adequacy in the capacity market

Katiraee

Jalili

2023

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…In such environment, the level of the uncertainty is very high [1]. Since the electricity market has many complexities and the electricity price fluctuates in different situations [2], it is better for a large consumer to use diverse kinds of resources such as power market, bilateral contracts, and self‐generation facilities including diesel units [3], Photovoltaic (PV) panels [4], and Wind Turbines (WT) [5]. Participating in Demand Response Programs (DRPs) is another reasonable way for large consumer to minimize its costs and risks.…”

Section: Introductionmentioning

confidence: 99%

A coordinated model for self‐scheduling and market participation of a large consumer in the face of a comprehensive set of uncertainties with incomplete Data

Heydarian‐Forushani

Abdollahi

Mehrizi

et al. 2023

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

In a restructured electricity market, a large consumer could supply its electricity consumption through its own available resources or purchasing from the electricity markets. Moreover, it could participate in demand response programs (DRPs) and also supply some portion of its demand from bilateral contracts. In this regard, the large consumer's objective is to minimize its own operation cost while facing several uncertainties that could affect its optimal decisions. The decision‐making problem becomes more difficult especially when the historical data is incomplete, or when the data is not available. This paper proposes an optimal decision‐making framework for a large consumer in the face of a comprehensive set of uncertain resources including electricity market price, internal electricity consumption, renewable power generation, and DR participation. To this end, a probabilistic‐possibilistic methodology is proposed to model the uncertainty of the electricity market prices in a more accurate way in comparison with existing approaches through the concept of Z‐number method. The uncertainty of internal electricity consumption is modelled by means of the seasonal ARIMA model, while the stochastic generation of its existing renewable resources are modelled through appropriate probability distribution functions. Moreover, the uncertainty of DRPs is considered by Markov chain. The large consumer also could sign bilateral contracts with generation companies to provide a portion of its required electricity demand. The simulation results confirm the effectiveness of the Z‐number methodology in comparison with other techniques for handling the uncertainties.

show abstract

“…Operating district heating systems and CHP plants to improve wind power integration has been investigated [49], as well as CHP investment trends in energy systems but without detailed analysis of operational patterns [50]. Concurrent optimization of investments and operation of CHP units have been analyzed on the microgrid level [51] and for a solar-aided CHP system [52]. However, to our knowledge, there is no publication that assesses both the optimal investments and dispatch of conventional CHP plants in relation to flexibility provision in a regional system context, in which both the electricity and district heating sectors are included.…”

Section: Introductionmentioning

confidence: 99%