Michael Angelo A. Pedrasa

Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.

Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization

Interruptible loads represent highly valuable demand side resources within the electricity industry. However, maximizing their potential value in terms of system security and scheduling is a considerable challenge because of their widely varying and potentially complex operational characteristics. This paper investigates the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 h. The scheduling objective is to achieve a system requirement of total hourly curtailments while satisfying the operational constraints of the available interruptible loads, minimizing the total payment to them and minimizing the frequency of interruptions imposed upon them. This multiobjective optimization problem was simplified by using a single aggregate objective function. The BPSO algorithm proved capable of achieving near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem. The effectiveness of the approach was further improved by dividing the swarm into several subswarms. The proposed scheduling technique demonstrated useful performance for a relatively challenging scheduling task, and would seem to offer some potential advantages in scheduling significant numbers of widely varied and technically complex interruptible loads.

Improved energy services provision through the intelligent control of distributed energy resources

There is a need to improve the delivery of energy services, and utilizing distributed energy resources offers significant potential. We propose an energy service modeling technique that would capture temporal variations of its demand and value, and differentiate it from the electric energy consumed by the end-use equipment. We then use this technique with a novel energy service simulation platform that aims to maximize the net benefit derived from energy services. The simulation platform creates a strategy for how available distributed resources should be operated in order to provide the desired energy services while minimizing the cost of consumption. The corresponding optimization problem is solved using particle swarm optimization. The simulation platform proved capable of creating an operation schedule that maximizes net benefit under a range of challenging conditions.

Robust scheduling of residential distributed energy resources using a novel energy service decision-support tool

This paper describes a methodology for making robust day-ahead operational schedules for controllable residential distributed energy resources (DER) using a novel energy service decision support tool. The tool is based on the consumers deriving benefit from energy services and not on electric energy. It maximizes consumer net benefit by scheduling the operation of DER. The robust schedule is derived using a stochastic programming approach formulated for the DER scheduler: the objective function describing the consumer net benefit is maximized over a set of scenarios that model the range of uncertainty. The optimal scenario set is derived using heuristic scenario reduction techniques. Robust operational schedules are formulated for a ‘smart’ home case study with four controllable DER under stochastic energy service demand, availability of storage DER, and status of dynamic peak pricing. The robust schedule results in a lower expected cost but at the expense of long computation times. The computation period however is not much of a disadvantage because schedules are computed off-line. The consumer can prepare several DER schedules and simply choose the one to implement according to their perception of the coming day. The robust schedules are formulated using an improved version of co-evolutionary particle swarm optimization.

The value of accurate forecasts and a probabilistic method for robust scheduling of residential distributed energy resources

We describe a decision-support tool that optimizes the energy services of residential end-users by scheduling the operation of available distributed energy resources. We discuss the application of the tool to a ‘smart’ home case study and the solution to the resulting highly-dimensional scheduling problem. We then use the optimal schedules formulated by the tool to determine the value of the forecasted information used when the schedules are created. This is achieved by computing the additional costs avoided by the end-users due to the accuracy of the forecasts. We also demonstrate how to use the tool to derive robust schedules when the end-users are not certain on the magnitude of solar insolation, magnitude of energy service demands, availability of a plug-in hybrid vehicle as storage, and status of Critical Peak Pricing. The robust schedule is derived by maximizing the expected net benefit when the schedule is applied to all likely scenario outcomes.

Optimal design of hybrid renewable energy system for electrification of isolated grids

This paper presents an algorithm for optimal design of hybrid renewable energy system consisting of wind turbines, photovoltaic modules, micro-hydro and biomass units. In this approach, unit cost of energy (

Information Exchange for Enhanced Network Selection

/kWh) is used to compare different renewable projects. For each renewable generating unit, energy cost is computed using the units levelized cost and annual energy production. An objective function with aim to minimize the average cost of electricity by reducing the energy purchased from existing diesel generators is designed. The developed algorithm selects the least cost combination of generators from a list of commercially available renewable units taking into account hourly demand, hourly renewable resources input and market availability of each renewable unit. To examine the proposed methodology, a computer program is developed and the simulation results are presented.

Low Cost Mechanical Load Emulator

Current devices use a network selection policy that is mostly driven by the physical layer, choosing the point of attachment with the highest Received Signal Strength Indicator (RSSI). Unfortunately for 802.11 networks, RSSI is not a good indicator of actual network performance as it is normally the bandwidth to the Internet and not the wireless signal conditions which dictates the quality of service a user might experience. Worse, the AP may belong to a pay service which renders it inaccessible to the user. MOBIX is a system which leverages on the fact that nodes on the move will meet other nodes who will be able to share conditions of networks they have recently used. MOBIX exchanges reports with other nodes it encounters using a short-range communication channel such as Bluetooth. Our simulation results show that exchanging throughput information resulted in 70% success rate over relying on RSSI measurements alone. Using our power measurements, we show that we can achieve energy savings of more than 80%.

Investigating the value of making hourly operational decisions for residential distributed energy resources

The mechanical load emulator is a device that is used to emulate a rotating mechanical load for electric motors. It consists of a DC generator that is connected to a programmable electronic load. The emulator can simulate three types of loads: constant-torque loads (T=K ₁), traction loads (T=K₂omega_m), and pump loads (T=K₃omega_m ²). The mechanical load emulator may be used for motor testing or instructional purposes

Implementation and comparison of modified binary particle swarm optimization approaches to an interruptible load dispatch problem

We investigate the value of making hourly operational decisions for residential distributed energy resources such as interruptible and shiftable appliances and energy storage. The value is determined by computing the savings achieved when making hourly decisions and comparing it to the savings achieved when making day-ahead decisions. These decisions, or schedules, are formulated considering the uncertainties in energy service demand and status of dynamic peak pricing. The robust schedules are generated using an energy service decision support tool we have presented in an earlier paper. We used the tool to formulate day-ahead schedules by maximizing the expected net benefit of the consumer over an optimal set of scenarios that represents the range of uncertainty, and the results were presented in another paper. In this paper, we used the tool to implement hour-by-hour decision-making by applying the rolling horizon model to the optimal scenario set approach. Based on the scenarios we simulated, the average savings is not significant enough to favor it over day-ahead scheduling. The day-ahead schedules, therefore, are already robust and improving it by making hourly decisions savings may not be enough to recover the expenses for the effort and equipment required to support real-time decision-making.