In addition to technological development (of prime movers, storage and balance-of- plant), consideration must be given to operational control of these µCHP systems. Control systems incorporate both equipment (such as sensors and interfaces with critical components such as fuel valves and pumps) and some form of logic (likely deployed as a software algorithm). The aim of the control system is to maximise the performance of the µCHP system as a whole, i.e. prime mover, auxiliary thermal generation, fuel sub-systems and storage sub-systems. Depending on the complexity of the control system, it may attempt to maximise one or more performance metrics, including carbon emission savings, economic cost saving and system lifetime. A well- designed control system will consider the operating constraints of each µCHP system component, incorporating the appropriate logic to maximise performance in light of such constraints. The effectiveness of control systems within the context of the variability of demand profiles is an important consideration for µCHP performance assessment.
In the context of micro-generation system operation and control, it is important to discriminate between operating regimes, control strategies, control signals and control drivers.
Operating Regimes dictate the manner in which the system operates, and what the system is designed to achieve. Common operating regimes, as discussed by Newborough [61], are summarised below, although a myriad of alternative or hybrid regimes could be conceived:
“Thermal Load Following”, where the system attempts to match thermal demand on a temporal basis
“Electrical Load Following”, where a similar approach is taken to electrical demand
“Continuous-Output Operation”, where the system is operated continuously for sustained periods of time, e.g. annual heating seasons
“Constant-Output Operation”, where the system is operated at constant load for one or more periods per day
“Autonomous Operation”, where the system satisfies all onsite energy demands without electrical grid support
Control Strategies are the methods by which the concept system responds to changes in demand. Multiple control strategies can be combined within the control algorithms of a µCHP system. Examples of control strategies include:
“Output Modulation”, usually of an electrical prime mover or thermal auxiliary, where the generator’s part load operation is constrained within certain energetic or temporal limits
“Thermal Store Temperature Control”, where the temperature of the storage medium drives operational state of a heat generating device
“Electrical Peak Shaving”, where electrical generation and storage operation is controlled to limit the electrical import power level
“Thermal Dumping”, in the context of thermal output from the prime mover that is purposely ejected to the external environment in order to allow the prime mover to continue generation of electricity. It is prudent to note that
micro-CHP systems deployed in the UK cannot, under the current regulatory framework, be designed to dump thermal output during normal operation
“Economic Optimisation”, where output is dispatched (i.e. directed or curtailed) based on some economic signal, for instance real-time electricity and/or fuel prices
Control Signals are derived from observation of a physical, or virtual, parameter that is used by the control algorithm to trigger a change in operating state. These parameters include:
Temperature of the internal air within the dwelling
State of charge of electrical storage
Temperature of thermal storage medium
Electrical demand
Energy output of non-dispatchable generation
Grid electrical import/export prices
Other network-derived generation/storage/export incentives
Control Drivers are the reason behind the control strategies implemented by a system operating under a specific operating regime. These drivers can be time-dependent or independent, and the Control Driver of a discrete dwelling may be considered as the satisfaction of a Control Signal from an external network, i.e. electrical grid or natural gas network. They include minimisation of fuel consumption and carbon footprint, maximisation of utilisation of on-site generation.
Harrison [86] argues that µCHP system operation is heat-led, as the prime mover attempts to satisfy some or all of the thermal demand, where electricity is generated as a by-product. This is contrary to the technical principles of prime mover operation, where otherwise wasted heat is recovered at a temperature useful for the building’s SH and/or DHW systems. Electricity is typically considered the premium output, as the carbon intensity of grid electricity is several times larger than the carbon intensity of thermal energy generated by a natural gas-fired boiler. However, the electrical efficiencies achieved by the majority of prime mover technologies discussed in Section
1.4 are too low to generate electricity with a carbon intensity less than or equal to that of the NEG, hence use of the recovered heat to displace boiler fuel is required in order for µCHP systems to reduce CO2 emissions. This concept is explored in Section 5.2.2.
Hawkes & Leach [58] state that heat-led control (i.e. thermal load following operating regime) has been the standard assumption of the µCHP industry. Simple thermal or electrical load following operating regimes assumes that the prime mover, and associated balance-of-plant, is capable of undergoing frequent thermal cycles. However, as discussed in Sections 1.4.2 and 1.4.3, µCHP systems based on certain prime movers suffer significant efficiency and lifetime penalties due to thermal cycling, and may have transient performance characteristics that vastly restrict output for long periods since start-up. To avoid these issues, it is important to develop more complex control systems, incorporating operating regimes and control algorithms to reduce the impact of such characteristics, and to achieve a good match between supply and demand. The latter entails an understanding of the thermal and electrical load profiles (and cumulative totals), and their levels of co-incidence, over the course of the day and year. Selecting a µCHP system design whose output can match the demand profile of a particular dwelling is essential in maximising performance and lifetime.
Various operating and control systems have been applied to modelled systems in the published literature, including thermal load following [61][3][81][77][76]; electrical load following [62][53][80][57][76]; continuous operation [62]; and least-cost control [58][81]. A previous study [45] explored the concept of centralised control of aggregated load from multiple µCHP systems, and such an approach has been brought to market by the German utility Lichtblick, using the ICE-based system discussed in Section 1.4.1. A number of investigations, both practical and modelling-based, that have considered various approaches to the control and operation of µCHP systems are discussed in Section 1.6.