24,364 research outputs found
Smart Grid for the Smart City
Modern cities are embracing cutting-edge technologies to improve the services they offer to the citizens from traffic control to the reduction of greenhouse gases and energy provisioning. In this chapter, we look at the energy sector advocating how Information and Communication Technologies (ICT) and signal processing techniques can be integrated into next generation power grids for an increased effectiveness in terms of: electrical stability, distribution, improved communication security, energy production, and utilization. In particular, we deliberate about the use of these techniques within new demand response paradigms, where communities of prosumers (e.g., households, generating part of their electricity consumption) contribute to the satisfaction of the energy demand through load balancing and peak shaving. Our discussion also covers the use of big data analytics for demand response and serious games as a tool to promote energy-efficient behaviors from end users
Minimizing the impact of EV charging on the electricity distribution network
The main objective of this paper is to design electric vehicle (EV) charging
policies which minimize the impact of charging on the electricity distribution
network (DN). More precisely, the considered cost function results from a
linear combination of two parts: a cost with memory and a memoryless cost. In
this paper, the first component is identified to be the transformer ageing
while the second one corresponds to distribution Joule losses. First, we
formulate the problem as a non-trivial discrete-time optimal control problem
with finite time horizon. It is non-trivial because of the presence of
saturation constraints and a non-quadratic cost. It turns out that the system
state, which is the transformer hot-spot (HS) temperature here, can be
expressed as a function of the sequence of control variables; the cost function
is then seen to be convex in the control for typical values for the model
parameters. The problem of interest thus becomes a standard optimization
problem. While the corresponding problem can be solved by using available
numerical routines, three distributed charging policies are provided. The
motivation is threefold: to decrease the computational complexity; to model the
important scenario where the charging profile is chosen by the EV itself; to
circumvent the allocation problem which arises with the proposed formulation.
Remarkably, the performance loss induced by decentralization is verified to be
small through simulations. Numerical results show the importance of the choice
of the charging policies. For instance, the gain in terms of transformer
lifetime can be very significant when implementing advanced charging policies
instead of plug-and-charge policies. The impact of the accuracy of the non-EV
demand forecasting is equally assessed.Comment: 6 pages, 3 figures, keywords: electric vehicle charging, electricity
distribution network, optimal control, distributed policies, game theor
Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids
Electric vehicle fleets and smart grids are two growing technologies. These technologies
provided new possibilities to reduce pollution and increase energy efficiency.
In this sense, electric vehicles are used as mobile loads in the power grid. A distributed
charging prioritization methodology is proposed in this paper. The solution is based
on the concept of virtual power plants and the usage of evolutionary computation
algorithms. Additionally, the comparison of several evolutionary algorithms, genetic
algorithm, genetic algorithm with evolution control, particle swarm optimization, and
hybrid solution are shown in order to evaluate the proposed architecture. The proposed
solution is presented to prevent the overload of the power grid
Optimized Household Demand Management with Local Solar PV Generation
Demand Side Management (DSM) strategies are of-ten associated with the
objectives of smoothing the load curve and reducing peak load. Although the
future of demand side manage-ment is technically dependent on remote and
automatic control of residential loads, the end-users play a significant role
by shifting the use of appliances to the off-peak hours when they are exposed
to Day-ahead market price. This paper proposes an optimum so-lution to the
problem of scheduling of household demand side management in the presence of PV
generation under a set of tech-nical constraints such as dynamic electricity
pricing and voltage deviation. The proposed solution is implemented based on
the Clonal Selection Algorithm (CSA). This solution is evaluated through a set
of scenarios and simulation results show that the proposed approach results in
the reduction of electricity bills and the import of energy from the grid
Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico
The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980–2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively
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