A novel energy scheduling framework for reliable and economic operation of islanded and grid-connected microgrids

Bashir, Arslan Ahmad; Pourakbari‐Kasmaei, Mahdi; Contreras, Javier; Lehtonen, Matti

doi:10.1016/j.epsr.2019.02.010

Cited by 50 publications

(31 citation statements)

References 42 publications

(65 reference statements)

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“…In addition, the houses are equipped with HVAC systems that provide the possibility for contribution in the DR program by adjusting their load. For this reason, a two-capacity model of the HVAC system, which is highly accurate and efficient for DR application, is used [22]. Furthermore, a stochastic optimization problem with different scenarios has been considered for addressing the uncertainty associated with wind and demand.…”

Section: A Indexes and Setsmentioning

confidence: 99%

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Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

Tavakkoli

Fattaheian‐Dehkordi

Pourakbari‐Kasmaei

et al. 2021

IEEE Trans. Sustain. Energy

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Section: A Indexes and Setsmentioning

confidence: 99%

“…-(22), (23)-(24), (25)-(26), and (27)-(28) are the dual constraints associated to ∆ , , , , , , , variables, respectively. In addition, constraints (29)-(38) indicate the sign for the aforementioned dual variables.…”

mentioning

confidence: 99%

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

Tavakkoli

Fattaheian‐Dehkordi

Pourakbari‐Kasmaei

et al. 2021

IEEE Trans. Sustain. Energy

Self Cite

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“…The heating or cooling load in a detached house is modeled using a two-capacity thermal model [3], [16]. The model has good accuracy in capturing the indoor air dynamics relative to the outdoor temperature changes.…”

Section: A Two-capacity Building Modelmentioning

confidence: 99%

“…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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“…DR is the one of the solutions to match the supply with demand by managing the demand (shifting uninterruptible or schedulable loads) unlike the other solutions where supply is regulated to match the demand. The DR programs deal with peak events effectively by changing the demands to match the available generation, and help to prevent the network from overloading [9][10][11]. In this way, DR helps to avoid electricity outages or blackouts, increases savings in electricity bills, and raises responsibility awareness in customers [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Costs of Demand Response from Residential Customers’ Perspective

2019

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Electricity demand in a certain locality varies during the day, depending on weather conditions, daily life routines, or a social event in a town. During high/peak demands, expensive power plants are put into operation, which affects electricity prices. Moreover, power lines are overloaded. If generation capacity is insufficient, a blackout may result. Demand response (DR) programs are widely proposed in energy research to tackle these problems. Although the benefits of DR programs are well known, customer response levels to these programs is low. This is due to the small fraction of benefits they receive against the loss of comfort, lost leisure time, and other inconveniences. The objective of this work is to study DR costs from the customer perspective by considering these factors. A customer survey-based direct approach is used to evaluate the willingness of customers to accept (WTA) a certain compensation when shifting the load is adopted. Two different methods are used to calculate DR costs: percentage compensation, which customers are WTA, and one based on a macroeconomic model, which considers the dependency factor of customers on loads and hourly wage. A linear mathematical model is presented based on both these techniques. This study reveals that DR costs are much less than interruption costs paid by the utility company, and hence is in the best interests of all stakeholders, i.e., customers, utility company, and transmission company.

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A novel energy scheduling framework for reliable and economic operation of islanded and grid-connected microgrids

Cited by 50 publications

References 42 publications

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

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

Costs of Demand Response from Residential Customers’ Perspective

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