Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems

Industrial process integration based on mixed integer linear programming has been used for decades to design and improve industrial processes. The technique has later been extended to solve multi-period and multi-scale problems for the design of urban energy systems. Assistance is indeed required for the elaboration of coordinated investment scheduling strategies to promote renewable and efficient urban energy infrastructure shaping the future energy context for the next decades. Major energy consumers, such as hospital complexes, airports, or educational campuses can act as a driving force for the development of renewable energy cities by attracting profitable large-scale energy networks and infrastructure. The proposed methodology generates optimal alternatives for the replacement, in a long-term perspective, of the various energy supply units and systems considering the evolution of the energy demand and the availability of the energy resources. Energy integration techniques are coupled to a parametric multi-objective optimization routine to select and size the energy equipment with both financial profitability and CO2 emission reduction as objectives. The originality of the developed method lies in the integration of a multi-period mixed integer linear programming formulation to generate long-term investment planning scenarios. The method has been demonstrated on a complex of eight hospitals totaling 466,000 m2 and an operating budget of 1.85 billion USD per year. The energy integration of new centralized and decentralized equipment has been evaluated on a monthly basis over four periods until the year 2035. The results show that among the four scenarios identified, the most optimistic alternative allows to decrease the final energy consumption of about 36%, cut the CO2 emissions by a half, multiply the renewable energy share by a factor 3.5 while reducing the annual total cost by 24%. This scenario considers mainly the integration of a very low temperature district heating with decentralized heat pumps to satisfy the heat requirements below 75°C, as well as heat recovery systems and the refurbishment of about 33% of the building stock.

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Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar

Applied Energy ◽

10.1016/j.apenergy.2021.116712 ◽

2021 ◽

Vol 290 ◽

pp. 116712

Author(s):

Niina Helistö ◽

Juha Kiviluoma ◽

Germán Morales-España ◽

Ciara O’Dwyer

Keyword(s):

Energy Systems ◽

Investment Planning ◽

Temporal Representations

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A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model

Resources ◽

10.3390/resources10050052 ◽

2021 ◽

Vol 10 (5) ◽

pp. 52

Author(s):

Annette Steingrube ◽

Keyu Bao ◽

Stefan Wieland ◽

Andrés Lalama ◽

Pithon M. Kabiro ◽

...

Keyword(s):

District Heating ◽

Energy Systems ◽

Energy System ◽

Heat Pumps ◽

Energy Simulation ◽

Heating Systems ◽

Optimal Amount ◽

Urban City ◽

Urban Energy ◽

Building Level

District heating is seen as an important concept to decarbonize heating systems and meet climate mitigation goals. However, the decision related to where central heating is most viable is dependent on many different aspects, like heating densities or current heating structures. An urban energy simulation platform based on 3D building objects can improve the accuracy of energy demand calculation on building level, but lacks a system perspective. Energy system models help to find economically optimal solutions for entire energy systems, including the optimal amount of centrally supplied heat, but do not usually provide information on building level. Coupling both methods through a novel heating grid disaggregation algorithm, we propose a framework that does three things simultaneously: optimize energy systems that can comprise all demand sectors as well as sector coupling, assess the role of centralized heating in such optimized energy systems, and determine the layouts of supplying district heating grids with a spatial resolution on the street level. The algorithm is tested on two case studies; one, an urban city quarter, and the other, a rural town. In the urban city quarter, district heating is economically feasible in all scenarios. Using heat pumps in addition to CHPs increases the optimal amount of centrally supplied heat. In the rural quarter, central heat pumps guarantee the feasibility of district heating, while standalone CHPs are more expensive than decentral heating technologies.

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A Typical Meteorological Year Generation Based on NASA Satellite Imagery (GEOS-I) for Sokoto, Nigeria

International Journal of Photoenergy ◽

10.1155/2014/468562 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 5

Author(s):

Olayinka S. Ohunakin ◽

Muyiwa S. Adaramola ◽

Olanrewaju M. Oyewola ◽

Richard L. Fagbenle ◽

Fidelis I. Abam

Keyword(s):

Energy Systems ◽

Building Energy ◽

Global Solar Radiation ◽

Dew Point ◽

Weather Data ◽

Maximum Temperature ◽

Building Energy Simulation ◽

Dew Point Temperature ◽

Northwest Region

Computer simulation of buildings and solar energy systems are being used increasingly in energy assessments and design. This paper evaluates the typical meteorological year (TMY) for Sokoto, northwest region, Nigeria, using 23-year hourly weather data including global solar radiation, dew point temperature, mean temperature, maximum temperature, minimum temperature, relative humidity, and wind speed. Filkenstein-Schafer statistical method was utilized for the creation of a TMY for the site. The persistence of mean dry bulb temperature and daily global horizontal radiation on the five candidate months were evaluated. TMY predictions were compared with the 23-year long-term average values and are found to have close agreement and can be used in building energy simulation for comparative energy efficiency study.

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