Karlis Baltputnis

As the EU countries are working on adapting the Electricity Directive to allow independent aggregation (IA) of demand response (DR) in all the electricity markets, this paper provides an assessment of potential benefits from DR in the day-ahead market, which has proven particularly challenging for the IA regulatory framework development. The model devised in this study uses data of the public wholesale market price curve from the Nord Pool power exchange to simulate market clearing results with introduction of certain amounts of DR that, via independent aggregation, competes alongside generation and is able to shift the supply curve. The simulated new market equilibrium point allows estimating price reduction capability of demand response, the total system-wide benefits, as well as analysing the potential remuneration mechanisms for independent aggregators and implications on their business models. While the results demonstrated a high value from DR during the peak hours, the overall benefits during average price periods were rather low, thus exposing the unpredictability of the revenue stream and questioning the business case for IA in the day-ahead market. The proposed approach can be used for further analysis of different IA compensation mechanisms, considering the system-wide benefits it brings to the wholesale market.

Electricity market participants need to optimize their processes to avoid being outcompeted by other traders. Consequently, decision-making methods, algorithms and tools related to large-scale energy storage technologies (scheduling, sizing) and cascaded hydropower plants (scheduling, hydroelectric set selection), as well as heating demand forecasting, which is a prerequisite for efficient combined heat and power plant participation in an electricity market.

Elektroenerģijas tirgus dalībniekiem nepieciešams optimizēt savus procesus, lai nezaudētu konkurences cīņā. Turklāt efektīva tirgus darbība ir atkarīga arī no uzņēmumiem un iestādēm, kas organizē un regulē to, t.i., tirgus operatoriem, energosistēmas operatoriem un politikas veidotājiem. Šajā disertācijā dalība elektroenerģijas tirgū un tā darbība ir apskatīta no vairākām pusēm, t.i., tiek piedāvātas lēmumu pieņemšanas atbalsta metodes, algoritmi un rīki gan elektroenerģijas tirgus dalībniekiem, gan politikas veidotājiem. Darbā izstrādātas lēmumu pieņemšanas atbalsta metodes, algoritmi un rīki saistībā ar liela apjoma enerģijas akumulācijas tehnoloģijām (darbības plānošana, ietilpības izvēle), kaskādē esošām hidroelektrostacijām (darbības plānošana, hidroagregātu izvēle) un ar siltumenerģijas pieprasījuma prognozēšanu, kas ir priekšnosacījums efektīvai koģenerācijas staciju dalībai elektroenerģijas tirgū. No otras puses, politikas veidotāju lēmumu pieņemšanas atbalsts realizēts ar modelēšanu, novērtējumu un rekomendācijām saistībā ar lielo koģenerācijas staciju ietekmi uz elektroenerģijas tirgu un no tā izrietošām iespējām mainīt šīm stacijām piešķirto valsts atbalstu. Šo atšķirīgo jautājumu kopīga pazīme ir mērķis palielināt elektroenerģijas tirgus darbības efektivitāti, taču no dažādām perspektīvām.

Accurate demand forecasting in district heating networks is an essential and imperative task in the everyday operation of both, the network itself and the heating energy suppliers. Multiple regression is one of the possible approaches to solving the forecasting problem with sufficient accuracy and little computational effort. This paper presents a polynomial regression model and offers several additions for its further improvement. It is found that grouping the model residuals by hour-of-day allows notably reducing the forecast error. The value of other modifications and the optimum size of the training set can vary over time, thus an automatic model parameter selection before each new forecast is advised.

The transmission system operators are entrusted with the task of ensuring stable and reliable functioning of the power system. In a modern power system, there are three groups of market actors who can be called upon by TSOs to aid in power system operation management: controllable generation, energy storage and controllable load. The owners of these assets can rightfully expect remuneration for their services from the TSO to recoup investment costs and operational expenses. This study is focused on poundage hydroelectric power plants and it strives to estimate the cost of operating reserve provision. Data of the large hydroelectric power plants located on the River Daugava is used for the calculation of costs and validation of the proposed algorithms.

The paper presents practical research based on a real-life case study of the Baltic power system for optimal activation of manual frequency restoration reserves. A software tool for the determination of the activation volume and time schedule is developed to facilitate the decision-making process of the transmission system operator in balancing of the power system within a coordinated balancing area. The study is important in light of the ongoing integration of balancing markets within the European Union and the subsequent need to develop an activation optimization function. Recently, regular balancing needs of the Baltic countries were covered by the neighboring Russian power system. The motivation for this study was moving towards local regulation as much as possible for energy dependence related and economic reasons. Moreover, development of the activation optimization function has served as one of the first steps towards creating a fully automated balancing system.

This paper explores the impact of large natural-gas-fired combined heat and power plants on electricity price in the Latvian bidding area of the Nord Pool power exchange. While these power plants sell the electricity generated in an energy-only market, they also benefit from state aid in the form of capacity payments. Therefore, the financial feasibility of their continued operation with varying degrees of support is also explored. The case study is based on the two largest cogeneration plants in Latvia which are both recipients of capacity payments. It was found that the participation of these plants in the day-ahead market can have a limiting effect on the electricity price in the Latvian bidding area; however, the support awarded to both power plants can be reduced while still maintaining their competitiveness in the energy-only market.

This paper discusses various forecasting tools that can be used in predicting the thermal load in district heating networks, focusing on day-ahead hourly planning as it is particularly important for cogeneration plants participating in electricity wholesale markets. Forecasts obtained by employing an artificial neural network are compared to a polynomial regression model. Their ability to supplement each other in a combined forecasting tool has been considered as well. Prediction inaccuracy cost is observed and suggested as evaluation criterion. The case studies are based on the district heating network in Riga, Latvia. Recorded data sets of temperature and heat demand are applied for thermal load prediction.

This paper examines the potential benefits smart and quickly controllable load can provide for transmission network congestion management if end-users give the transmission network operator or an independent aggregator service access to disconnect their devices in case of contingencies. The necessary amount of available quickly controllable load for the fulfilment of N-1 criteria is found for the case study of the Latvian power system. Of particular interest is the possibility to increase the maximum available transfer capacity between the Latvian and Estonian bidding areas by removing the N-1 provision from the calculation of the cross-border trading capacity if it can be supplied by the controllable load instead. For this purpose, a steady-state power flow model of the 330 kV transmission network and its interconnections with neighboring countries is used for simulations.