Distributed and Market-Based Coordination

Due to increasing grid decentralisation, some authors have developed busi- ness models for inter-district energy trading and bidding. This moves beyond time-of-use tariffs, which set relatively regular pricing conditions each day, to a

Figure 2.6: The USEF model of interactions between energy network stake- holders [293]

more real-time energy market. Many of the solutions outlined in the previous sections are based on centralised control and optimisation which may not be acceptable from a privacy or user comfort point of view in a multiple stakeholder district. The studies reviewed in this section deploy a decentralised optimisa- tion strategy for which each individual consumer retains more control of their own energy management but operated within a wider market-based system. The most developed standard on an integrated energy market is given in the Universal Smart Energy Framework by the USEF Foundation, [293]. It clearly defines several stakeholders including the prosumer, the balance responsible party (BRP), the distribution system operator (DSO) and the transmission sys- tem operator (TSO). It outlines the interactions the stakeholders’ have with each other and the key role an energy aggregator can play to provide flexibility in the system, Figure 2.6. The grid can request flexibility at specific times from a series of aggregators which in turn manage a portfolio of prosumers from which it can leverage and negotiate flexibility. Once agreement is reached and the decisions have been actuated, the grid must financially compensate the prosumer for their flexibility service according to pre-agreed conditions. Zhou et al. [294] developed a two-level control strategy for an aggregator or the BRP. Using day ahead forecasting for demand and generation, the optimi- sation strategy aims the minimise the cost of intra-day energy trading required due to unforeseen circumstances or poor predictions.

Fanti et al. [295], developed a district energy management system based on day-ahead pricing schemes and real-time power monitoring. In this model, buildings are required to submit day ahead energy consumption predictions. Then the actual consumption of the buildings is monitored and compared to the estimations to determine rewards or penalties. A demand response (DR) aggregator for a group of residential buildings is presented in [296]. The con- troller bids for energy based on real-time pricing fluctuations set by the DSO. This allows empowered consumers to shift their load, avoiding peak prices, to achieve cost savings. This is also greatly beneficial to the DSO as overall peak demands on the system will be reduced. In [297], a community con- troller acts as a virtual DSO to implement real time price variation to a group of smart homes. Domestic appliances operation times are shifted to reduce the peak energy demand. However, in this case study some residences receive increased costs even though the overall cost for the district is reduced. This raises crucial issues of potential unfairness that could arise.

2.4.3.1 Multi-Agent Systems Approaches

Agent-based controllers in the context of real-time price variation can also be found in the literature. Multi-Agent Systems (MAS) have the advantages of a completely scalable computing architecture, resilient to failures in communica- tion, and potentially increased security as no agent will have access to every piece of information. The PowerMatcher software, a MAS market-based in- frastructure, is detailed in [298]. PowerMatcher was utilised in [299]. In this study the author proposed that consuming and producing appliances are rep- resented by intelligent agents. These agents submit the price that they are willing to pay or receive for their energy. Once all bids are assembled, the market clearing price is calculated. If this price is higher than the consumer agent is willing to pay, then it does not consume energy and waits for the next round. In a case study, the rate of over or underproduction from a wind farm is reduced by 50% and peak load is reduced. PowerMatcher was also used in [300]. It found that if the percentage of intelligent loads within a large district was increased, there is an almost linear decrease in peak power by up to 20%. PowerMatcher was enhanced in [301] to consider both electricity and heat in an integrated way which is important considering the increasing electrification of heat through devices like heat pumps.

Lagorse et al. [302], applied MAS to a hybrid renewable system. Each device had internal control logic and a ‘token’ is passed between devices to indicate which agent is in control of the overall DC voltage. The token is re- quested and passed between devices depending on their internal conditions.

Wide-scale MAS control of domestic appliances was simulated in [303]. An agent was based in the smart meter of each home representing the aggrega- tion of several controllable and uncontrollable household appliances. A con- nected system of 5000 homes was theorised and in a simulated case study, energy peaks were decreased by up to 17%. The GRENAD, MAS framework was outlined in [304]. This framework aimed to provide a generic, modular and flexible platform to simulate and control smart power grids using MAS. A novel, semantic web ontology based on existing standards and the USEF framework was developed in [305]. The ontology aimed to provide a data infrastructure on which a MAS energy management platform could be deployed.

2.4.3.2 Game Theory Approaches

More traditional optimisation methods focussed at a district-level could lead to overall system optimal e.g. minimum total cost of district energy but could lead to cost rises for specific individuals within the district. These issues of unfair- ness could potentially be resolved by instead using a game theory approach to solving district energy management problems. Game theory approaches can more fairly model individual ‘players’ rational desire to minimise their own energy costs. Saad et al. [36] provides an excellent review of the game theory applications in a smart grid environment. The autonomous, distributed, and heterogeneous nature of the smart grid make game theory well suited to smart grid problems. The review argues that interactions and energy trading between microgrids and the wider network can be modelled as well as interactions be- tween the consumer and utility company regarding demand side management and load shifting.

A two-level demand side management game is developed in [306]. The lower level evolutionary game composes of a population of residential, house- hold consumers choosing how much energy to purchase at specific hours from different utility companies based on their prices. The upper game is a non- cooperative game between the utility companies where they determine their generation amount and future energy price. Both games are proven to con- verge quickly, the method is shown to be scalable and results in a lower aver- age price for the consumers and a lower peak to average ratio. Gkatzikis et al. [307] investigates the role an aggregator can play in a future smart grid setting. A three-level scenario involving 10000 households, several aggregators and a single utility is investigated. A day ahead, the utility advertises a demand shifting target and a price they are willing to pay for this. The aggregator then bids a certain level of demand shifting on behalf of their portfolio of households whom they compensate for their flexibility. The system is shown to be highly

dependent on the reward the utility is likely to offer and the level of flexibility shown by the residential consumer. However, the study did show potential for a 15% reduction in operating costs where all three parties gain compared to a baseline, flat price scenario. A combined MILP and game theoretical approach is used in [308] to optimise the scheduling of controllable appliance to minimise the cost to a group of residential consumers working cooperatively. Wu et al. [309] uses game theory to optimally control household appliances of several residential consumers in an islanded microgrid with wind and gas generation. Using this method reduces the community energy bill by 38% even with imper- fect, Markov chain, wind generation forecasts. If the forecasts are improved a further 21% saving could be achieved.

Rather than considering appliance scheduling, [310] and [311] use a game theoretical approach to optimise a smart grid in which a small percentage of users have dispatchable electricity generation and / or storage capacity. It as- sumes day ahead knowledge of user demands from which a pricing tariff is set. The active users then use their flexibility to minimise their own electric- ity bills which results in a flatter demand profile and hence lower prices. The users with greater flexibility (generation and storage) achieve very high savings around 80% but even the passive users see a reduction in cost around 15% simply due to the reduction in peak prices. Mohsenian-Rad et al. [312] sug- gests that dynamic pricing set by the utility encourages each individual user reduce their energy cost by reducing their peak to average ratio. However, the author argues that this is not necessary providing a district works cooperatively to ensure their collective peak to average ratio is small. To achieve this the au- thors’ develop a distributed, game theoretical approach to minimise a collective district energy bill by scheduling their appliances iteratively and broadcasting their forecast energy consumption to their neighbours. This results in a 17% reduction in peak to average ratio and 18% reduction in cost.

2.4.4

Summary

This section demonstrates that when at a district level, the majority of opti- misation studies aim to optimise the supply side of the energy infrastructure. This largely involves scheduling controllable energy generation devices and energy storage capacity around stochastic renewable energy supply fluctuat- ing energy tariffs. In general, the demand-side is modelled simplistically, often assuming a fixed demand profile viewed as a constraint to be met within the optimisation. The demand profile is often considered to be perfectly forecast with no errors, which is unrealistic when deployed in real case studies. This

means that many of the reported energy or cost savings would be reduced and user comfort constraints may be impinged. It is likely that an intermediate error management step would be required to adjust the schedule provided by the optimisation to fit with updated, observed constraints.

Despite the influence of non-linear part-load characteristics as discussed in Section 2.3.1, energy generation models are often simplified to ensure an entirely linear problem to allow the use of linear programming techniques such as MILP. Effort should be made to include part-load characteristics as well as minimum operational loads, ramp up rates and cool down periods to ensure feasible optimised solutions. There is a conflict between centralised and de- centralised optimisation strategies within the literature. Decentralised solutions claim to be rapidly scalable, more secure, and more considerate of individual users preferences. Centralised optimisation strategies are more likely to find a global optimal. The literature has demonstrated that this can lead to poten- tial issues of unfairness with some buildings scheduled to consume at higher pricing periods for the greater good of district as a whole.