Day-Ahead Rolling Window Optimization of Islanded Microgrid with Uncertainty

Bashir, Arslan Ahmad; Lehtonen, Matti

doi:10.1109/isgteurope.2018.8571781

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…The two-capacity building model is accurate enough to estimate the space heating load of a detached house or an apartment building [10,12,23]. The model evaluates the heating demand by capturing the indoor air dynamics relative to the outdoor air temperature variation.…”

Section: Two-capacity Building Modelmentioning

confidence: 99%

“…Another necessary condition is that the sum of SOS2 variables must be equal to unity, which is implemented in Equation (18). The Constraints (19) and (20) describe the operation of DWHP in heat injection mode following the same linearization procedure as in Equations ( 21)- (23). To ensure that both heat extraction and heat injection do not occur simultaneously, Equation ( 24) is applied.…”

Section: Optimization Modelmentioning

confidence: 99%

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Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities

et al. 2021

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To achieve a successful integration of fluctuating renewable power generation, the power-to-heat (P2H) conversion is seen as an efficient solution that remedies the issue of curtailments as well as reduces carbon emissions prevailing in the district heating (DH) sector. Concurrently, the need for storage is also increasing to maintain a continuous power supply. Hence, this paper presents a MILP-based model to optimize the size of thermal storage required to satisfy the annual DH demand of a community solely by P2H conversion employing renewable energy. The DH is supplied by the optimal operation of a novel 2-km deep well heat pump system (DWHP) equipped with thermal storage. To avoid computational intractability, representative time steps with varying time duration are chosen by employing hierarchical agglomerative clustering that aggregates adjacent hours chronologically. The value of demand response and the effect of interannual weather variability are also analyzed. Numerical results from a Finnish case study show that P2H conversion utilizing small thermal storage in tandem with the DWHP is able to cover the annual DH demand, thus leading to a carbon-neutral DH system and, at the same time, mitigating the curtailment of excessive wind generation. Compared with the annual DH demand, an average thermal storage size of 29.17 MWh (2.58%) and 13.99 MWh (1.24%) are required in the business-as-usual and the demand response cases, respectively.

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Section: Two-capacity Building Modelmentioning

confidence: 99%

Section: Optimization Modelmentioning

confidence: 99%

Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities

et al. 2021

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“…The country-wide flexibility potential of domestic space heating loads was considered in [14] in order to quantify the optimal size of centralized storage for renewable energy integration. In [15], [16], the DR capability of space heating and electric water heater loads was studied to minimize the power curtailments in a residential microgrid. In [17], the flexibility of space heating loads was envisioned for distribution network management during high penetrations of wind power.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Wind Power Curtailment and Carbon Emissions by Power to Heat Sector Coupling—A Stackelberg Game Approach

et al. 2020

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This paper proposes a Stackelberg game approach to minimize both the wind power imbalances and carbon emissions by harnessing demand response (DR) of residential heating loads fed by electricity or district heating (DH) options. The problem is formulated as a bilevel optimization model. An aggregator (leader) at the upper level aims at minimizing the mismatch between electricity demand and wind power while mitigating the carbon emissions arising from the DH system. The aggregator owns a wind farm and is responsible for controlling the DH generation through a combustion-based source and a deep well heat pump system (DWHP), which converts power into heat by utilizing wind power and replacing combustion-based DH. The aggregator submits bonuses to the households (followers) at the lower level incentivizing them to modify their consumption profile. The households receive the bonus offers and consequently decide how to optimize their net electricity and DH energy payments via an upward or downward DR strategy. The uncertainties associated with wind power and heating loads are considered using a stochastic programming framework. Long term thermal performance of the DWHP is studied separately. Results prove that the proposed bilevel framework enables significant reductions in wind power imbalances, carbon emissions, and energy payments. INDEX TERMS Stackelberg game, demand response, district heating, deep well heat pump, bilevel optimization, power to heat, sector coupling

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“…Moreover, numerous studies have been performed to integrate high wind power into the system while utilizing the flexibility offered by responsive loads. In Reference [10], the flexibility of HVAC loads coordinated with building thermal masses were studied to mitigate the curtailments in islanded networks. The power sink capabilities of HVAC and EV loads under variable wind generation penetrations were analyzed in [11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Coordination of Aggregated Hydro-Storage with Residential Demand Response in Highly Renewable Generation Power System: The Case Study of Finland

Bashir

Lehtonen

2019

Energies

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Current energy policy-driven targets have led to increasing deployment of renewable energy sources in electrical grids. However, due to the limited flexibility of current power systems, the rapidly growing number of installations of renewable energy systems has resulted in rising levels of generation curtailments. This paper probes the benefits of simultaneously coordinating aggregated hydro-reservoir storage with residential demand response (DR) for mitigating both load and generation curtailments in highly renewable generation power systems. DR services are provided by electric water heaters, thermal storages, electric vehicles, and heating, ventilation and air-conditioning (HVAC) loads. Accordingly, an optimization model is presented to minimize the mismatch between demand and supply in the Finnish power system. The model considers proportions of base-load generation comprising nuclear, and combined heat and power (CHP) plants (both CHP-city and CHP-industry), as well as future penetration scenarios of solar and wind power that are constructed, reflecting the present generation structure in Finland. The findings show that DR coordinated with hydropower is an efficient curtailment mitigation tool given the uncertainty in renewable generation. A comprehensive sensitivity analysis is also carried out to depict how higher penetration can reduce carbon emissions from electricity co-generation in the near future.

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Day-Ahead Rolling Window Optimization of Islanded Microgrid with Uncertainty

Cited by 7 publications

References 13 publications

Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities

Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities

Minimizing Wind Power Curtailment and Carbon Emissions by Power to Heat Sector Coupling—A Stackelberg Game Approach

Optimal Coordination of Aggregated Hydro-Storage with Residential Demand Response in Highly Renewable Generation Power System: The Case Study of Finland

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