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New York City’s food distribution system is among the largest in the United States. Food is transported by trucks from twelve major distribution centers to the city’s point-of-sale locations. Trucks consume large amounts of energy and contribute to large amounts of greenhouse gas emissions. Therefore, there is interest to increase the efficiency of New York City’s food distribution system. The Gowanus district in New York City is undergoing rezoning from an industrial zone to a mix residential and industrial zone. It serves as a living lab to test new initiatives, policies, and new infrastructure for electric vehicles. We analyze the impact of electrification of food-distribution trucks on greenhouse gas emissions and electricity demand in this paper. However, such analysis faces the challenges of accessing available and granular data, modeling of demands and deliveries that incorporate logistics and inventory management of different types of food retail stores, delivery route selection, and delivery schedule to optimize food distribution. We propose a framework to estimate truck routes for food delivery at a district level. We model the schedule of food delivery from a distribution center to retail stores as a vehicle routing problem using an optimization solver. Our case study shows that diesel trucks consume 300% more energy than electric trucks and generate 40% more greenhouse gases than diesel trucks for food distribution in the Gowanus district. 

In this paper, we propose the greedy smallest-cost-rate path first (GRASP) algorithm to route power from sources to loads in a digital microgrid (DMG). Routing of power from distributed energy resources (DERs) to loads of a DMG comprises matching loads to DERs and the selection of the smallest-cost-rate path from a load to its supplying DERs. In such a microgrid, one DER may supply power to one or many loads, and one or many DERs may supply the power requested by a load. Because the optimal method is NP-hard, GRASP addresses this high complexity by using heuristics to match sources and loads and to select the smallest-cost-rate paths in the DMG. We compare the cost achieved by GRASP and an optimal method based on integer linear programming on different IEEE test feeders and other test networks. The comparison shows the trade-offs between lowering complexity and achieving optimal-cost paths. The results show that the cost incurred by GRASP approaches that of the optimal solution by small margins. In the adopted networks, GRASP trades its lower complexity for up to 18% higher costs than those achieved by the optimal solution. 

In this paper, we experimentally demonstrate the performance of the recently proposed Energy Packet Switch (EPS) for energy distribution. The N × M EPS aggregates the energy from N sources and dispatches energy to M outputs, each of which feeds one or many loads. Energy is distributed from a source to a load in the form of energy packets. The operation of the EPS is an enabler device to realize a digital microgrid. We carry out exhaustive experiments to show that the EPS grants energy to keep demand satisfied and even in cases when the demand overwhelms the EPS capacity. Results of the experiments show that the EPS ably grants all energy requests that fall within its capacity, and it controls the distribution of energy under extenuating conditions by approaching a level of fairness. The experiments also show the average time that a request waits for the corresponding grant. 

The Digital Power Network (DPN) is an energy-on-demand approach. In terms of Internet of Things (IoT), it treats the energy itself as a `thing' to be manipulated (in contrast to energy as the `thing's enabler'). The approach is mostly appropriate for energy starving micro-grids with limited capacity, such as a generator for the home while the power grid is down. The process starts with a request of a user (such as, appliance) for energy. Each appliance, energy source or energy storage has an address which is able to communicate its status. A network server, collects all requests and optimizes the energy dissemination based on priority and availability. Energy is then routed in discrete units to each particular address (say air-condition, or, A/C unit). Contrary to packets of data over a computer network whose data bits are characterized by well-behaved voltage and current values at high frequencies, here we deal with energy demands at highvoltage, low-frequency and fluctuating current. For example, turning a motor ON requires 8 times more power than the level needed to maintain a steady states operation. Our approach is seamlessly integrating all energy resources (including alternative sources), energy storage units and the loads since they are but addresses in the network. Optimization of energy requests and the analysis of satisfying these requests is the topic of this paper. Under energy constraints and unlike the current power grid, for example, some energy requests are queued and granted later. While the ultimate goal is to fuse information and energy together through energy digitization, in its simplest form, this micro-grid can be realized by overlaying an auxiliary (communication) network of controllers on top of an energy delivery network and coupling the two through an array of addressable digital power switches. 

In the current grid, power is available at all times, to all users, indiscriminately. This makes the grid vulnerable to sporadic demands and much effort has been invested to mitigate their effect. We offer here a digital approach to power distribution: an energy-on-demand approach in which the user initiates an energy request to the server of the energy provider before receiving the energy. Considering a micro-grid with a mix of generators (sustainable and other sources), the server optimizes the entire power network before granting the energy requests, fully or partially. The energy is packetized and is routed to the user's address by an array of switches. For example, in an office building, the energy provider may queue energy requests by some air-condition units and grant these requests later. During recovery from a blackout, pockets of instability may be isolated by their unusual energy demands. In its simplest form, this network can be realized by overlaying an auxiliary (control, or, data) network on top of an energy delivery network and coupling the two through an array of addressable digital power switches. In assessing this approach, we are concentrating in this paper on the management of energy requests by using statistical models. An energy network with a limited channel capacity and the optimal path for energy flow in a standard IEEE 39 bus are considered.