Real-Time Demand Response Through Aggregate Electric Water Heaters for Load Shifting and Balancing Wind Generation

Pourmousavi, S. Ali; Patrick, Stasha N.; Nehrir, M.H.

doi:10.1109/tsg.2013.2290084

Cited by 139 publications

(84 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the difference is very small. Actually, the expected power generations of all micro-turbine generators obtained by considering different standard deviations of PV power generations are almost the same because two location parameters of PV power generations in Equation (14) are utilized. These location parameters are centered at the same mean value for a given PV array although different standard deviations are considered and different location parameters are gained in Equation (14).…”

Section: Comparison Of Results Considering Different Standard Deviatimentioning

confidence: 99%

“…DR can also be achieved by alternative approaches, such as aggregated load control [14,15], plug-in electric vehicles [16] and frequency-related control [17]. Pourmousavi et al evaluated the thermostat setpoint control of aggregate electric water heaters for load shifting, and for providing a desired balancing reserve for the utility.…”

Section: Introductionmentioning

confidence: 99%

“…Pourmousavi et al evaluated the thermostat setpoint control of aggregate electric water heaters for load shifting, and for providing a desired balancing reserve for the utility. This work also assessed the economic benefits of DR for customers considering time-of-use pricing [14]. Salinas et al considered a third-party's management of the energy consumption of a group of users, and formulated the corresponding load scheduling problem as a constrained multi-objective optimization problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies

Hong

2017

Applied Sciences

View full text Add to dashboard Cite

Abstract:The demand response and accommodation of different renewable energy resources are essential factors in a modern smart microgrid. This paper investigates the energy management related to the short-term (24 h) unit commitment and demand response in a factory power system with uncertain photovoltaic power generation. Elastic loads may be activated subject to their operation constraints in a manner determined by the electricity prices while inelastic loads are inflexibly fixed in each hour. The generation of power from photovoltaic arrays is modeled as a Gaussian distribution owing to its uncertainty. This problem is formulated as a stochastic mixed-integer optimization problem and solved using two levels of algorithms: the master level determines the optimal states of the units (e.g., micro-turbine generators) and elastic loads; and the slave level concerns optimal real power scheduling and power purchase/sale from/to the utility, subject to system operating constraints. This paper proposes two novel encoding schemes used in genetic algorithms on the master level; the point estimate method, incorporating the interior point algorithm, is used on the slave level. Various scenarios in a 30-bus factory power system are studied to reveal the applicability of the proposed method.

show abstract

Section: Comparison Of Results Considering Different Standard Deviatimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies

Hong

2017

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Among the various residential load types, thermostatic loads such as air conditioner (AC) and water heater (WH) loads, and deferrable loads such as electric vehicle (EV) loads are placed at the top of the merit list of DR appliances due to their greater load shifting potential and larger daily energy demand share [14][15][16]. Recently, the load management benefits of controlled appliances for the increased deployment of intermittent renewable generation have been investigated [17][18][19][20][21]. A stochastic unit commitment model is presented for assessing the reserve requirements resulting from the integration of renewable sources in [17], where alternative DR paradigms for assessing the benefits of demand-side flexibility on absorbing the variability and uncertainty of renewable supply are investigated.…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

2017

View full text Add to dashboard Cite

Abstract:The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC) units to smooth the system's imbalance, considering the real-time DR potential (DRP) and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC), water heater (WH), electric vehicle (EV) loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers' comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.

show abstract

“…DSM has also been demonstrated as being viable for 114 provision of balancing reserve for wind generation [9].…”

mentioning

confidence: 99%

Demand side management of an urban water supply using wholesale electricity price

et al. 2017

View full text Add to dashboard Cite

Municipal water supply consumes large quantities of electrical energy to move water from catchment areas to service reservoirs near centres of population. Pumping does not necessarily occur round the clock, but rather when necessary to uphold constraints relating to reservoir levels and system pressure. There is a degree of flexibility in the timing of pumping that makes it an excellent candidate for Demand Side Management, meaning that it can provide opportunities for improving power system operation and reducing electricity costs for the water utility. The extent of this flexibility depends on a number of factors. This study examines the optimisation of two water supply systems -the 'Van Zyl' benchmark system and a representation of the supply for the city of Belfast, Northern Ireland. The potential to employ intelligent operation of pumps to help bolster uptake of variable wind generation is assessed, as is quantification of the potential savings for a water utility. The results show significant potential savings for the water utility as well as a substantial increase in the utilisation of wind power.

show abstract

Real-Time Demand Response Through Aggregate Electric Water Heaters for Load Shifting and Balancing Wind Generation

Cited by 139 publications

References 13 publications

Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies

Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

Demand side management of an urban water supply using wholesale electricity price

Contact Info

Product

Resources

About