The Role of Demand Side Management in the Economics of Microgrids1

Microgrid design entails the selection and sizing of appropriate energy generation and storage technology to meet a known or proposed load profile. For traditional generation technology (i.e. thermal generating plants), it is well known that the incremental costs of meeting high peak demand is much higher than the average cost and hence negatively impacts microgrid economics. On the other hand, for microgrids with high renewable penetration, the situation is more complicated. The times of greatest resource constraint are generally not coincident with peak demand. In this study, we will discuss techniques of identifying the times of greatest resource constraint, methods of addressing them through efficiency measures and demand response, and the economic impacts of this approach. Studies to date have indicated that demand response programs that are implemented as little as 5% of the time can save nearly 50% in capital costs. Introduction & Background: In many respects, the process of microgrid design echoes the process that electric utilities have been following since Edison begin the first commercial operation in 1882[1]. With a knowledge of the load, the generation and distribution systems have to be sized to accommodate the worst case condition, the peak load. In addition, since the grid demand has historically been consumer driven, grid operators are required to provide a capacity surplus (operating reserve) to accommodate unanticipated loads and/or outages[2]. While the simplest solution would be a single power plant with nameplate capacity 10% higher than the known peak, this solution is also quite impractical for a number of reasons. Single large power plants typically don’t follow load very well and new load is not easy to accommodate without significant capital expense for new plants. Utilities approach the problem with a fleet of generators with various characteristics. On one end of the spectrum are large thermal plants (typically coal or nuclear) that are expensive to build but can generate electricity at a relatively low per kWh rate. On the other extreme are single cycle combustion turbines that are capable of much better variable performance and can be built in small increments (10’s of MW) with lower up-front costs but the variable costs are relatively higher. The 1 Presented at the 2016 Energy Policy Research Conference, Santa Fe, NM, September 9-11, 2016. 2 optimal combination can be found using load screening curves that compare the annual load arranged from peak to valley (Load Duration Curve) with curves representing the variable and fixed costs of each generation technology. One of the most consistent results of these economic optimization exercises is that the cost per kWh to meet peak demand is often much higher (integral multiples) than the costs to meet the so-called base load, or “always on” portion of the demand profile[3]. The concept of the microgrid has become a mainstream concept over the past two decades, largely driven by the potential for better integration of renewable resources and their inherent variability. The vast majority of the literature, and indeed, the experience of microgrids presupposes a tie to the larger grid, thus minimizing the need to provide 100% of the generation, capacity reserve and the additional constraints needed to maintain reliability[4]. Much has been written on relationship between microgrid control and cost of operations. An extensive review of the literature in this area can be found in [4] . The work by Chen et al. [5] presents an economic optimization with both renewable and traditional generators and Alvarez discusses a fast optimal dispatch scheme using a heuristic approach[6]. All of these works focus on optimization of the grid operation in the absence of Demand Response. Several researchers have developed optimal approaches to microgrid operation utilizing demand response. Cha demonstrated a multi-agent control scheme utilizing fuzzy logic for both generation dispatch and demand response[7]. Korkas [8] looks and demand response and thermal energy storage while a discussion of DR and biomass generation can be found in [9] . These works contribute to the body of literature that is helping to define the economic value of demand response as an operational concept. However, the ability of DR to reduce capital cost is not considered in any of these. The initial design of the microgrid is not often discussed, largely because it is considered as an add-on to an existing, and expansive electric grid. In other words, the microgrid contains the number and capacity of generation that happen to be installed and the challenge is to control the grid in such a way that minimizes overall cost, often dominated by power purchases from the larger grid. For stand-along grids, the design constraints are more demanding. One can approach microgrid design in a similar manner, but in the process, one discovers that the screening curve approach assumes that the operator has control of when and how much each generator produces. Such is clearly not the case for wind and solar resources. Another approach to microgrid design would be to simply estimate (or measure) how many kWh are required in a given year, compare that to the typical solar and wind resources of the region and size the generation (solar panel array or wind turbines) so that the expected output meets the demand, plus some reserve to account for atypical climate and unforeseen loads. This approach is flawed because it rests on the assumption that the timing of the consumption is arbitrarily flexible and can be altered to accommodate generation. In fact, we find that a large portion of demand takes place when (and often because) typical renewable sources are not available. In order to compensate for those times when the demand cannot be met with the solar and wind resources, energy storage is introduced to the system. Batteries are currently the most effective means of spreading the demand of the load throughout the day. This is true because that during the peak solar production hours of the day, an excess of electricity is produced. The batteries act as a buffer between the energy production, determined by natural processes and the load, determined by consumer behavior.