Bi-level particle swarm optimization and evolutionary algorithm approaches for residential demand response with different user profiles

Carrasqueira, Pedro; Alves, Maria João; Antunes, Carlos Henggeler

doi:10.1016/j.ins.2017.08.019

Cited by 48 publications

(26 citation statements)

References 13 publications

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“…Cplex), although requiring many binary variables. Examples of such models are the ones presented in [11] and [12]. However, the MILP modelling of the thermostat behavior is more complex and imposes a higher computational effort to obtain the optimal solution.…”

Section: Mathematical Models Of a Thermostatically-controlled Ac Systemmentioning

confidence: 99%

A Discussion of Mixed Integer Linear Programming Models of Thermostatic Loads in Demand Response

Antunes

Rasouli

Alves

et al. 2019

Trends in Mathematics

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In smart grids, it is expected that electricity retailers will offer timedifferentiated tariffs with significant price variations in short periods. Consumers are then encouraged to engage in demand response strategies by making the most of their flexibility in the operation of end-use appliances to minimize the electricity bill without compromising on comfort. Air conditioning systems are particularly suited to be controlled, by profiting from the thermal inertia of building units. This paper presents novel mixed integer linear programming formulations to optimize the operation of a thermostatically-controlled air conditioning system, thoroughly discussing their main features and associated computational difficulties resulting from their combinatorial nature.

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Section: Mathematical Models Of a Thermostatically-controlled Ac Systemmentioning

confidence: 99%

A Discussion of Mixed Integer Linear Programming Models of Thermostatic Loads in Demand Response

Antunes

Rasouli

Alves

et al. 2019

Trends in Mathematics

Self Cite

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“…; this operation scheme is not feasible in practice but it does not violate constraints (11). Thus, constraints (13) are imposed, which ensure that each load j starts its operation (stage r=1) at most at time T2j  dj + 1 so that it can finish until T2j, i.e. within its allowed time slot.…”

Section: Datamentioning

confidence: 99%

“…within its allowed time slot. Constraints (11) together with (12) and (13) ensure that load j is operating exactly dj consecutive time intervals, forcing yjrt to be 0 when load j is "off". …”

Section: Datamentioning

confidence: 99%

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A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing

Alves

Antunes

2018

Computers & Operations Research

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Presently, residential electricity consumers are, in general, charged at flat or dual time-of-use tariffs along the day, which are defined by the retailer for long periods (e.g., one year). These pricing schemes do not convey price signals reflecting generation costs and grid conditions. Hence, consumers lack the incentives to engage in different consumption patterns using the flexibility they generally have in the operation of some end-use loads. Dynamic tariffs, i.e. energy prices varying possibly with significant magnitude in short periods of time, are expected to become an applicable pricing scheme in smart grids. In this setting, home energy management systems can play an important role to help end-users optimizing the usage of appliances to minimize energy costs without compromising comfort. This can be advantageous also from the perspective of grid management.A semivectorial bilevel programming approach is developed to model the interaction between electricity retailers and consumers in order to optimize electricity time-of-use retail pricing. The aim is to support the retailer in finding good decisions for the prices. The retailer (upper level decision maker) establishes dynamic time-of-use electricity prices to maximize profits. The consumer (lower level decision maker) responds by selecting, under that price setting, a load scheduling decision leading to a nondominated solution balancing his objectives of minimizing the electricity bill (cost dimension) and minimizing the dissatisfaction in face of his preferences and requirements (comfort dimension).The lower level optimization problem is formulated as a bi-objective mixed-integer linear programming problem. A hybrid approach is proposed, which consists of a genetic algorithm for the upper level problem and an exact solver to solve surrogate scalar problems at the lower level. A case study is presented and discussed.

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“…The Quasi-static Artificial Bee Colony approach is used to optimize a multi-objective DR problem, based on the cost of energy and peak demand at the building level [49], including PV, Combined Heat and Power (CHP), batteries, electrical energy from the grid, and natural gas. Particle Swarm Optimisation is used in [50] to solve a bi-level problem modelling the interaction between the retailer and consumers. The energy hub is explored in [51] to develop a multi-carrier Demand-Side Management Time of Use (DSM ToU) optimization balancing energy import, conversion, and storage.…”

Section: Introductionmentioning

confidence: 99%

Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

et al. 2018

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Demand Response (DR) is a fundamental aspect of the smart grid concept, as it refers to the necessary open and transparent market framework linking energy costs to the actual grid operations. DR allows consumers to directly or indirectly participate in the markets where energy is being exchanged. One of the main challenges for engaging in DR is associated with the initial assessment of the potential rewards and risks under a given pricing scheme. In this paper, a Genetic Algorithm (GA) optimisation model, using Artificial Neural Network (ΑΝΝ) power predictions for day-ahead energy management at the building and district levels, is proposed. Individual building and building group analysis is conducted to evaluate ANN predictions and GA-generated solutions. ANN-based short term electric power forecasting is exploited in predicting day-ahead demand, and form a baseline scenario. GA optimisation is conducted to provide balanced load shifting and cost-of-energy solutions based on two alternate pricing schemes. Results demonstrate the effectiveness of this approach for assessing DR load shifting options based on a Time of Use pricing scheme. Through the analysis of the results, the practical benefits and limitations of the proposed approach are addressed.

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Bi-level particle swarm optimization and evolutionary algorithm approaches for residential demand response with different user profiles

Cited by 48 publications

References 13 publications

A Discussion of Mixed Integer Linear Programming Models of Thermostatic Loads in Demand Response

A Discussion of Mixed Integer Linear Programming Models of Thermostatic Loads in Demand Response

A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing

Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions

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