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A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants' willingness to pay for thermal comfort with a bottom-up, nonlinear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a "brick wall" preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 1% reduction in costs vs. a similar "brick-wall" MPC approach with the same comfort and 6–11% reduction in costs vs. other control strategies in the literature. CCPSO can also be used to operate the building with much greater comfort and costs or much lower costs and comfort than the "brick-wall" approach, depending on user preferences. CCPSO also reduced peak-hours demand by 3% vs. the "brick-wall" strategy and 4–14% vs. other strategies. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% or more vs. other control methods. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours. [ABSTRACT FROM AUTHOR]

The use of renewable energies, such as wind and solar, is rising in microgrids. However, because of their intermittent nature, achieving a stable system in spite of the fluctuations of generation and demand is very difficult. Aiming to increase the stability margin of AC islanded microgrids comprising nondeterministic resources, properly share real power (P), and keep the network frequency (f) at nominal value, this study exploits the presence of energy storage units (ESUs) along with a load-shedding scheme. To this end, this study proposes a multitier decentralized control strategy that is based on frequency and includes three control schemes, distributed generation unit (DGU) control, ESU control, and local loads control (LLC). These controllers operate simultaneously. The DGU controller shares the loads among DGUs in accordance with the frequency droop method. On the other hand, the ESU controller controls the production of ESUs and also the LLCs manage the loads and dump-load of the microgrid. This method ensures the proper operation of AC microgrids that comprise intermittent resources under stand-alone conditions and despite numerous events. The time responses of P and f prove the high performance of the method. The simulations are performed and coded in m-file of MATLAB. [ABSTRACT FROM AUTHOR]

In electric power grids, generation must equal load at all times. Since wind and solar power are intermittent, system operators must predict renewable generation and allocate operating reserves to mitigate imbalances. If they overestimate the renewable generation during scheduling, insufficient generation will be available during operation, which can be very costly. However, if they underestimate the renewable generation, usually they will only face the cost of keeping some generation capacity online and idle. Therefore overestimation of renewable generation resources usually presents a more serious problem than underestimation. Many researchers train their solar radiation forecast algorithms using symmetric criteria like RMSE or MAE, and then a bias is applied to the forecast later to reflect the asymmetric cost faced by the system operator – a technique we call indirectly biased forecasting. We investigate solar radiation forecasts using asymmetric cost functions (convex piecewise linear (CPWL) and LinEx) and optimize directly in the forecast training stage. We use linear programming and a gradient descent algorithm to find a directly biased solution and compare it with the best indirectly biased solution. We also modify the LMS algorithm according to the cost functions to create an online forecast method. Simulation results show substantial cost savings using these methods. [ABSTRACT FROM AUTHOR]